Reviewerly/exHarmony huggingface dataset:
Blockchain Enabled Credibility Applications: Extant Issues, Frameworks and Cases

The credibility of information is known as a major cause of a wide range of issues, such as: altered product information in food supply chains; fake transactions on E-commerce platforms; lengthy claim settlement time in agricultural insurance; and costly borrowings in agricultural financing. For a more specific example in food supply chains, end customers want to check the product information, but either doubt the authenticity of information, or simply do not have access to the information. The reason is that upstream suppliers and downstream retailers are often reluctant to share data, fearing privacy loss or business secret leakage. The consequence is that regulatory departments may face enormous challenges to identify accurate contamination sources, if there is scarce information or falsely recorded information at any stage of the food supply chain. In this paper, we focus on four common scenarios demanding information credibility in the agricultural supply chain: product traceability, E-commerce platforms, agricultural insurance, and agricultural financing. We review some high-profile smart credibility applications with emphasis on how blockchain related technologies can provide the information credibility by examining extant issues and relevant frameworks.
New perspective on trace element (Re, Ge, Ag) hosts in the Cu-Ag Kupferschiefer deposit, Poland: Insight from a LA-ICP-MS trace element study

The Kupferschiefer deposit in SW Poland provides an opportunity to study an ore system where besides factors such as temperature, salinity and pH, redox conditions play a major role in controlling trace element incorporation in sulphides. Chalcocite, djurleite, bornite, chalcopyrite, sphalerite, covellite and pyrite from the Cu-Ag deposit in the Lubin-Sieroszowice district were analysed with laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) to study the distribution of trace elements and to identify the main carriers of by-products such as silver and rhenium. Trace element anomalies are not only associated with certain minerals but also linked to specific mineralization styles. Chalcopyrite in epigenetic veins has significantly higher content of Ge, As and Sb while sulphides adjacent to hematitic "red spots" are enriched in redox sensitive elements: djurleite is enriched in V, Se, Re, Au, Hg and Tl, chalcopyrite in V, Se, Hg and Tl, and pyrite in Tl in comparison with bulk Cu-Ag mineralization. Elevated and substantially more variable data for V, Mn, Ni, Co, Ga, Mo, Sn in shale hosted sulphides can be attributed to submicron inclusions of Kupferschiefer silicate matrix which might skew low concentration values even if careful petrographic observations and precise spot selection are applied. Silver concentrations form a linear pattern from chalcocite/djurleite > bornite > chalcopyrite > sphalerite. Silver distribution in the deposit on a broad scale could be linked to the general Fe3+-Cu-Pb-Zn-Fe2+ zonation as well as mineral sequence (hematite-chalcocite-bornite-chalcopyrite-galena-sphalerite-pyrite) observed in the majority of sediment hosted stratiform copper deposits. Epigenetic sulphide-calcite veins associated with local faults and tectonic zones contain chalcopyrite exceptionally rich in germanium (up to 4806 μg/g of Ge) suggesting that the potential of Kupferschiefer Cu-Ag ores in terms of Ge grades and resources could be underestimated. The majority of analysed sulphides do not contain rhenium above detection limits (0.1 μg/g Re) with measurable quantities found almost exclusively in djurleite (up to 3.9 μg/g Re). This indicates that copper sulphides, in particular djurleite, could be a preferred host and an important carrier of rhenium in the deposit.
Improving neural topic modeling via Sinkhorn divergence

Textual data have been a major form to convey internet users’ content. How to effectively and efficiently discover latent topics among them has essential theoretical and practical value. Recently, neural topic models(NTMs), especially Variational Auto-encoder-based NTMs, proved to be a successful approach for mining meaningful and interpretable topics. However, they usually suffer from two major issues:(1)Posterior collapse: KL divergence will rapidly reach zeros resulting in low-quality representation in latent distribution; (2)Unconstrained topic generative models: Topic generative models are always unconstrained, which potentially leads to discovering redundant topics. To address these issues, we propose Autoencoding Sinkhorn Topic Model based on Sinkhorn Auto-encoder(SAE) and Sinkhorn divergence. SAE utilizes Sinkhorn divergence rather than problematic KL divergence to optimize the difference between posterior and prior, which is free of posterior collapse. Then, to reduce topic redundancy, Sinkhorn Topic Diversity Regularization(STDR) is presented. STDR leverages the proposed Salient Topic Layer and Sinkhorn divergence for measuring distance between salient topic features and serves as a penalty term in loss function facilitating discovering diversified topics in training. Several experiments have been conducted on 2 popular datasets to verify our contribution. Experiment results demonstrate the effectiveness of the proposed model.
DSAS: A Secure Data Sharing and Authorized Searchable Framework for e-Healthcare System

In e-healthcare system, an increasing number of patients enjoy high-quality medical services by sharing encrypted personal healthcare records (PHRs) with doctors or medical research institutions. However, one of the important issues is that the encrypted PHRs prevent effective search of information, resulting in the decrease of data usage. Another issue is that medical treatment process requires the doctor to be online all the time, which may be unaffordable for all doctors (e.g., to be absent under certain circumstances). In this paper, we design a new secure and practical proxy searchable re-encryption scheme, allowing medical service providers to achieve remote PHRs monitoring and research safely and efficiently. Through our scheme DSAS, (1) patients’ healthcare records collected by the devices are encrypted before uploading to the cloud server ensuring privacy and confidentiality of PHRs; (2) only authorized doctors or research institutions have access to the PHRs; (3) Alice (doctor-in-charge) is able to delegate medical research and utilization to Bob (doctor-in-agent) or certain research institution through the cloud server, supporting minimizing information exposure to the cloud server. We formalize the security definition and prove the security of our scheme. Finally, performance evaluation shows the efficiency of our scheme.
Anomaly detection with vision-based deep learning for epidemic prevention and control

Abstract During the COVID-19 pandemic, people were advised to keep a social distance from others. People’s behaviors will also be noticed, such as lying down because of illness, regarded as abnormal conditions. This paper proposes a visual anomaly analysis system based on deep learning to identify individuals with various anomaly types. In the study, two types of anomaly detections are concerned. The first is monitoring the anomaly in the case of falling in an open public area. The second is measuring the social distance of people in the area to warn the individuals under a short distance. By implementing a deep model named You Only Look Once, the related anomaly can be identified accurately in a wide range of open spaces. Experimental results show that the detection accuracy of the proposed method is 91%. In the social distance, the actual social distance is calculated by calculating the plane distance to ensure that everyone can meet the specification. Integrating the two functions and implementing the environmental monitoring system will make it easier to monitor and manage the disease-related abnormalities on the site.
Evaluation of solid wastes in the manufacture of ceramic tableware glazes

The recovery and reuse of industrial wastes are of great importance to the environment. Today, a large amount of waste is produced due to marble production, and it is extremely important to bring such wastes to the ceramic industry, to solve the pollution problem and to provide cost-efficient ceramic production. In this study, marble waste was used for ceramic earthenware glaze and body formulations. Marble waste was used in two different formulations as glaze and body. Chemical analysis, morphological features, crystallographic information of different formulations were made with XRF, SEM and XRD, respectively. When the hardness test results of the ceramic body and glazed samples were examined, the hardness values of the marble waste added samples increased by 1.9% compared to the standard samples. According to the results obtained, it has been seen that using marble waste in the ceramic industry is very important for both economic and environmental reasons.
Evaluation of a blockchain‐based dynamic consent platform (METORY) in a decentralized and multicenter clinical trial using virtual drugs

Blockchain is a novel data architecture characterized by a chronological sequence of blocks in a decentralized manner. We aimed to evaluate the real-world feasibility of a blockchain-based dynamic consent platform (METORY) in a decentralized and multicenter trial. The study consisted of three visits (i.e., screening and 2 follow-up visits) with a 2-week interval. Each subject was required to report the self-measured body temperatures and take a virtual investigational drug by entering the unique drug code on the application. To simulate real-world study settings, two major (i.e., changes in the schedule of body temperature measurement) and three minor protocol amendments (i.e., nonsignificant changes without any changes in the procedures) were set. Overall study completion rates, proportion of consent, and response time to each protocol amendment and adherence were evaluated. A total of 60 subjects (30 in each center) were enrolled in two study centers. All subjects completed the study, and the overall proportion of consent to each protocol amendment was 95.7 ± 13.7% (mean ± SD), with a median response time of 0.2 h. Overall, subjects took 90.8% ± 19.2% of the total drug, whereas compliance with the schedule was 69.1% ± 27.0%. Subjects reported 96.7% ± 4.2% of the total body temperature measurements whereas the adherence to the schedule was 59.0% ± 25.0%, which remarkably decreased after major protocol amendments. In conclusion, we evaluated a blockchain-based dynamic consent platform in real clinical trial settings. The results suggested that major changes should be avoided unless subjects' proper understanding is warranted.
Zero-Trust Artificial Intelligence?

Any critical artificial intelligence (AI)-based product or service should be continuously questioned and evaluated. This suggests a “zero-trust” or “trust, but verify” approach to AI.
Privacy-Preserving Boolean Range Query with Temporal Access Control in Mobile Computing

With increasingly popular GPS-equipped mobile devices (e.g., smartphones, tablets, laptops), massive spatio-textual data has been outsourced to cloud servers for storage and analysis such as spatial keyword search. However, existing privacy-preserving spatial keyword query schemes only support coarse-grained non-temporal access control in single-user sharing scenarios, which does not scale well in time-related scenes such as message valid period. To solve the above issues, we propose Privacy-preserving Boolean Range Query with Temporal access control in mobile computing (PBRQ-T). Specifically, we first achieve PBRQ with linear search complexity using the adapted Gray code, Bloom filter, and Katz-Sahai-Waters encryption. Then, we provide fine-grained and temporal access control in PBRQ based on the forward/backward derivation function and attribute-based encryption, where PBRQ is executed only when the spatio-textual data is accessible. Finally, an enhanced PBRQ-T (i.e., PBRQ-T+) with faster-than-linear search complexity is proposed by constructing a Quadtree index structure. Our formal security analysis shows that data privacy and index privacy can be guaranteed during the query process. Our extensive experiments using a real-world dataset demonstrate the efficiency and feasibility of our schemes.
A perfect secret sharing scheme for general access structures

The secret sharing (SS) scheme has been widely used as the de facto paradigm for group key management in cryptography and distributed computation. An SS scheme for a general access structure (GSS) has drawn more and more attention since it allows flexible access control. However, previous solutions to GSS need to either assign multiple shares to each participant or to solve a complex nonlinear integer programming. In this paper, we propose a novel strategy to address the two problems simultaneously based on the Chinese Remainder Theorem (CRT) for a polynomial ring over a finite field. We classify general access structures satisfying the monotone property into the two families of maximum forbidden subsets and minimum qualified subsets conforming to the security condition and the revealing condition. The moduli used in the scheme are pairwise coprime polynomials and can take the irreducible polynomials. To find the degrees of these polynomials, we only need to solve an integer linear programming (ILP) by minimizing the sum of the degrees of all the moduli. The proposed scheme is inherently a weighted SS scheme and may have no solution which commonly exists in all GSS schemes. To ensure a solution, we put forth a preprocessing algorithm which separates the original access structure into several subaccess structures based on graph theory. The proposed scheme only assigns a share for each participant and achieves perfect security. It has good generalization and provides a universal approach to program the scheme construction for SS schemes with general access structures, threshold access structures and weighted access structures.
Reducing Block Propagation Delay in Blockchain Networks via Guarantee Verification

With the development of blockchain technology, people always expect that blockchain can be applied to other fields. However, the low Transaction Processing Speed (TPS) and broadcast delay of blockchain still restrict the application of blockchain. To solve these problems, we propose a new scheme named GVScheme to improve the scalability of blockchain network. GVScheme introduces the role of guarantor based on trust value mechanism. The guarantor node will guarantee the block spread in the network. When the node receives the guarantee block from the guarantor node, the order of verification block and propagation block will be determined according to the trust value of the guarantor. By reducing the block verification time, the block propagation delay in the network will also be reduced. It is worth mentioning that our scheme keeps the minimum modification to the blockchain, and may even be directly applied to the blockchain network. Simulation results show that GVScheme can effectively reduce block propagation delay and the fork rate in blockchain network. When the block size and the number of nodes increase, GVScheme also shows great performance. Thus, under the same fork rate, the blockchain using GVScheme can allow less mining interval and larger block size limit.
Learning from the CNN-based Compressed Domain

Images are transmitted or stored in their compressed form and most of the AI tasks are performed from the re-constructed domain. Convolutional neural network (CNN)-based image compression and reconstruction is growing rapidly and it achieves or surpasses the state-of-the-art heuristic image compression methods, such as JPEG or BPG. A major limitation of the application of the CNN-based image compression is on the computation complexity during compression and reconstruction. Therefore, learning from the compressed domain is desirable to avoid the computation and latency caused by reconstruction. In this paper, we show that learning from the compressed domain can achieve comparative or even better accuracy than from the reconstructed domain. At a high compression rate of 0.098 bpp, for example, the proposed compression-learning system has over 3% absolute accuracy boost over the traditional compression-reconstruction-learning flow. The improvement is achieved by optimizing the compression-learning system targeting original-sized instead of standardized (e.g., 224x224) images, which is crucial in practice since real-world images into the system have different sizes. We also propose an efficient model-free entropy estimation method and a criterion to learn from a selected subset of features in the compressed domain to further re-duce the transmission and computation cost without accuracy degradation.
Visualization of Aqueous Geochemical Data Using Python and <scp>WQChartPy</scp>

Graphical methods have been widely used for visualization, classification, and interpretation of aqueous geochemical data to obtain a better understanding of surface and subsurface hydrologic systems. This method note presents WQChartPy, an open-source Python package developed to plot a total of 12 diagrams for analysis of aqueous geochemical data. WQChartPy can handle various data formats including Microsoft Excel, comma-separated values (CSV), and general delimited text. The 12 diagrams include eight traditional diagrams (trilinear Piper, Durov, Stiff, Chernoff face, Schoeller, Gibbs, Chadha, and Gaillardet) and four recently proposed diagrams (rectangle Piper, color-coded Piper, contour-filled Piper, and HFE-D) that have not been implemented in existing graphing software. The diagrams generated by WQChartPy can be saved as portable network graphics (PNG), scalable vector graphics (SVG), or portable document format (PDF) files for scientific publications. Jupyter and Google Colab notebooks are available online to illustrate how to use WQChartPy with example datasets. The geochemical diagrams can be generated with several lines of Python codes. Source codes of WQChartPy are publicly available at GitHub (https://github.com/jyangfsu/WQChartPy) and PyPI (https://pypi.org/project/wqchartpy/).
Two-Stage Supervised Discrete Hashing for Cross-Modal Retrieval

Recently, hashing-based multimodal learning systems have received increasing attention due to their query efficiency and parsimonious storage costs. However, impeded by the quantization loss caused by numerical optimization, the existing cross-media hashing approaches are unable to capture all the discriminative information present in the original multimodal data. Besides, most cross-modal methods belong to the one-step paradigm, which learn the binary codes and hash function simultaneously, increasing the complexity of optimization. To address these issues, we propose a novel two-stage approach, named the two-stage supervised discrete hashing (TSDH) method. In particular, in the first phase, TSDH generates a latent representation for each modality. These representations are then mapped to a common Hamming space to generate the binary codes. In addition, TSDH directly endows the hash codes with the semantic labels, enhancing the discriminatory power of the learned binary codes. A discrete hash optimization approach is developed to learn the binary codes without relaxation, avoiding the large quantization loss. The proposed hash function learning scheme reuses the semantic information contained by the embeddings, endowing the hash functions with enhanced discriminability. Extensive experiments on several databases demonstrate the effectiveness of the developed TSDH, outperforming several recent competitive cross-media algorithms.
Gender and power work relationships;: A systematic review on the evidence from Africa and Asia

The global community has been unanimous about the negative implications gender discrimination holds for economic growth and human development. While gender and power have been widely researched in the broader context of their influences on political participation, household decision-making, and overall health outcomes within various settings globally, consolidated literature on gender and power relations in organisations in the developing world remains sparse. This paper systematically reviews current knowledge on workplace gender and power relations from Africa and Asia. The review compiles past and recent studies to help scholars and practitioners better understand; the theoretical and empirical trends on gender and power relations; the unfolding trends; and how they influence employment outcomes in the labour markets of Africa and Asia. A three-stage strategy was adopted to systematically identify 67 peer-reviewed research papers from article databases to summarise the direction of scholarship. While this review generally uncovers a growing interest in workplace gender and power issues and highlights increased attention towards female economic empowerment, it also reveals that differences still exist between men and women in their use of power within organisations. These gaps perpetuated by individual, societal and organisational factors, create productivity losses due to limitations placed on women's enrolment into the labour force and their managerial prospects.
Hierarchical Proxy-based Loss for Deep Metric Learning

Proxy-based metric learning losses are superior to pair-based losses due to their fast convergence and low training complexity. However, existing proxy-based losses focus on learning class-discriminative features while overlooking the commonalities shared across classes which are potentially useful in describing and matching samples. Moreover, they ignore the implicit hierarchy of categories in real-world datasets, where similar subordinate classes can be grouped together. In this paper, we present a framework that leverages this implicit hierarchy by imposing a hierarchical structure on the proxies and can be used with any existing proxy-based loss. This allows our model to capture both class-discriminative features and class-shared characteristics without breaking the implicit data hierarchy. We evaluate our method on five established image retrieval datasets such as In-Shop and SOP. Results demonstrate that our hierarchical proxy-based loss framework improves the performance of existing proxy-based losses, especially on large datasets which exhibit strong hierarchical structure.
The Integration of Blockchain With IoT in Smart Appliances

An amazing utilization of internet of things (IoT)-based smart technologies has been observed in recent years. Internet of things (IoT) became smarter and autonomous, leading the way by interconnecting and interacting with the environment. Existing technologies such as big data, AI, and cloud computing have been supported by IoT but with some limitations. Blockchain is the better solution to access and share data with the decentralized system. IoT spreads its applications in healthcare, industrial internet of things, supply chain management, traffic management, etc., out of which smart cities take special attention in recent days by integrating with IoT to create a sustainable ecosystem by using information and communication technology (ICT). Smart cities can offer various smart applications for boosting the life prominence of society. These characteristics of blockchain are beneficial to the creation of smart cities. In this regard, the authors have conducted a systematic review of articles. This study highlights the integration of blockchain with IoT.