Reviewerly/exHarmony huggingface dataset:
Privacy-Preserving Public Auditing for Regenerating-Code-Based Cloud Storage

Cloud computing is one of evolving technology nowadays, giving versatile services. However, secure information sharing is vulnerable to cloud computing. With cloud storage services, customers can remotely keep their information to the cloud and recognize the data sharing with others. Access management is a troublesome task to share sensitive information on cloud servers. Remote data integrity auditing is proposed to guarantee the integrity of the information stored in the cloud. The cloud data might contain some sensitive information it should no longer be exposed to others when the cloud report is shared. Encrypting the entire shared file can recognize the sensitive data hiding; however, it will make this shared report not able to be utilized by others. At pick time the server not able to serve the entire request at the time. In order to address this problem, we propose a remote data integrity auditing scheme and secure Data sharing with time constraints mechanism in Cloud Computing to protect outsourced data in cloud storage against corruptions, adding fault tolerance to cloud storage together with data integrity checking and failure reparation becomes critical. Recently, regenerating codes have gained popularity due to their lower repair bandwidth while providing fault tolerance. Existing remote checking methods for regenerating-coded data only provide private auditing, requiring data owners to always stay online and handle auditing, as well as repairing, which is sometimes impractical. In this research, author proposes a public auditing scheme for the regenerating-code-based cloud storage. To solve the regeneration problem of failed authenticators in the absence of data owners, we introduce a proxy, which is privileged to regenerate the authenticators, into the traditional public auditing system model. Moreover, we design a novel public verifiable authenticator, which is generated by a couple of keys and can be regenerated using partial keys. Thus, our scheme can completely release data owners from online burden. In addition, we randomize the encode coefficients with a pseudorandom function to preserve data privacy. Extensive security analysis shows that our scheme is provable secure under random oracle model and experimental evaluation indicates that our scheme is highly efficient and can be feasibly integrated into the regenerating-code-based cloud storage.
Afl-Dag: Asynchronous Federated Learning with Directed Acyclic Graph-Based Blockchain in Edge Computing

Federated learning (FL) has emerged as a promising solution to enable privacy preserving machine learning for edge computing, while the development of machine learning (ML) aggravates data privacy concerns during model training. However, due to the distributed characteristics of FL, there are critical issues such as the vulnerability of global models to anomalies and the difficulty of coordination due to heterogeneous device resources, especially in an Internet of Things (IoT) based environment. To address the above limitations, asynchronous FL has been introduced to improve the efficiency of FL by reducing the latency of ML model training. Meanwhile, blockchain and directed acyclic graph (DAG)-based ledger also has drawn much attention to integration with FL due to the decentralization and byzantine fault tolerant consensus. Inspired by the peculiar characteristics of DAG consensus, which benefit asynchronous FL in terms of performance and security, this article presents a survey on the integration of asynchronous FL with DAG, which we call AFL-DAG. Motivated by the lack of a concrete taxonomy of asynchronous FL and a global model, we propose universally applicable terminologies, the so-called reference model and global model, and a straightforward horizontal classication of FL. Then, we introduce a system model of AFL-DAG for edge computing applications and identify several main consideration factors in AFL-DAG design. The state-of-the-art AFL-DAG solutions are summarized, and future directions to achieve practical AFL-DAG are highlighted. Finally, we investigate the application of AFL-DAG in a popular edge computing domain, on-device crowdsourcing, and provide an evaluation of AFL-DAG compared to blockchained-FL.
Parallel Solution to Spatial Contextualization for Closed Itemset Mining

Mining closed itemsets in transactional databases is essential in identifying unlabeled data. One of the characteristics of this area of knowledge is the computational cost inherent in the itemsetsâ€™ mining process. Therefore, a desirable improvement is to define parallel algorithms scalable by the number of CPU cores. In this work, we present the P arallel Solution to S patial Contextualization for C losed I temset M ining (PSCIM). Our algorithm explores the spatial organization of the data in the Solution Space defined by the Dual Scaling mapping to retrieve closed itemsets given the relative location of items. Previous approaches, like the SCIM algorithm, also use the Dual Scaling mapping while mining itemsets. The advantages of the PSCIM over the SCIM are: (i) it replaces the FP-Tree structure used by the SCIM algorithm to organize the data points with the LCM structure, which has linear complexity in enumerating all possible closed itemsets; (ii) it is designed for multicore processing; and (iii) its heuristics for itemset clustering is suitable not only for dense databases but also for sparse databases. Experiments in several databases showed that the PSCIM algorithm had, on average, a 3.3x speedup on a 4-core processor. Furthermore, we show that PSCIM has a better performance of mean all-confidence values for the retrieved closed itemsets when compared to state-of-art algorithms.
Transaction Fees and Seigniorage in Proof-of-Work Cryptocurrencies

I study the optimal design of transaction fees and seigniorage for a Proof-of-Work cryptocurrency. Commodity merchants need blockchain miners to record their payments and secure the blockchain by remaining active. Fees make miners willing to process merchantsâ€™ transactions by compensating for the risk that doing so slows down block transmissions making blocks invalid. Seigniorage convinces miners to operate when pending transactions are scarce. Both seigniorage and fees are necessary. As fees are distortionary for merchants, an optimal design uses them only as required by incentive-compatibility.
Online Attendance Face Recognition and QR Code Monitoring

In online era colleges and schools are facing problems regarding attendance issue, since writing attendance by hands could be a tough job for teachers and mainly it is a time-consuming task. By utilizing this framework it would be easy for students to enter class without going to the teacher for attendance, his/her attendance will be marked based on their face match and QR recognition which is based on Deep Learning. The model will also be used in times when the student would have to wear a mask, working of the model will ensure his/her iris, eyebrows, and face structure are synchronized with the real-time stored data in the database.From this teachers would be able to see the attendance of students and attendance will be marked automatically in the database. The model gives an accurate persistency and can be used in times of covid where work from home is essential and wearing a mask is an important aspect when we are outside.
(Retracted) Digital immersive interactive experience design of museum cultural heritage based on virtual reality technology

With the upsurge of urban construction, public buildings such as museums have attracted more and more attention as the business card of the city. The museum business is developing rapidly, and the artistic concepts appearing in museums are gradually differentiated and conceptualized, paying more attention to the experience and feeling of the visitors. With the continuous intervention of high technology and new media, the visual expression of museums has become more diverse. With the advent of virtual reality (VR) technology, more and more technological means have been introduced into the design of museum exhibits. It eliminates the boring feeling of the general public about the original exhibition method of the museum, while enriching the fun of visiting the museum. To provide visitors with an immersive experience in which virtual and reality intersect, we study the design of a digital immersive interactive experience for museum cultural heritage based on VR technology. By combining current technological development and policy advantages, we describe the innovative ideas and design schemes of museums applying VR and augmented reality technology and discuss the practical value and importance of establishing designs through experiments. The experimental data show that the use of VR technology to digitally design the cultural heritage of museums enhances the immersion and interaction of tourists in the experience, and the score for the VR model was 9.23% higher than that of the traditional forms, which shows that VR promotes the dissemination of the cultural heritage of museums.
JSON-based control model for SQL and NoSQL data conversion in hybrid cloud database

Abstract A data interaction transformation model, XYJSON, that is suitable for all data using current standard SQL syntax and JSON document data is proposed to solve the problem of increasing development workload and difficulty caused by using different control methods for corresponding types of databases under the cloud hybrid storage. A control program was studied to control relational and NoSQL data at the same time, by establishing a general conversion model between relational and NoSQL data and converting standard SQL statements into JSON. The performance of XYJSON was compared with that of the traditional mode. The results show that the performance difference between XYJSON and the traditional mode is small. In addition, a developer survey was conducted on XYJSON for user friendliness and compatibility. All developers rated XYJSON as excellent. The current cloud hybrid storage cannot use a unified control model to realize data control. XYJSON breaks through this bottleneck, making it easier and more efficient to control different types of databases under cloud hybrid storage.
ImDeeplabV3plus with instance selective whitening loss in domain generalization semantic segmentation

Semantic segmentation is a classical problem in computer vision, which is important in the field of autonomous driving. Although significant progress has been achieved in semantic segmentation, its generalization ability to unknown domains is still challenging. To effectively solve this problem, a semantic segmentation method ImDeeplabV3plus with instance selective whitening loss is proposed in this paper. DeeplabV3plus is selected as the baseline. In order to enhance the representation of the region of interest, the coordinate attention (CA) mechanism is added. To better integrate multiple low-level features, the adaptively spatial feature fusion (ASFF) is employed to adaptively learn the importance of features at different levels for each location. For preferably coping with the domain changes, an instance selective whitening (ISW) loss is introduced in the early stage of the backbone. The model is trained with the Cityscapes dataset and then applied to the unknown domain RobotCar dataset. Compared with DeeplabV3plus, the authors’ ImDeeplabV3plus model shows 1.29% mIoU improvement. When ISW loss is added, 2.08% improvement in mIoU is achieved compared with ImDeeplabV3plus. Experimental results show that the proposed method is simple and improves the domain generalization ability.
Optimizing differentially-maintained recursive queries on dynamic graphs

Differential computation (DC) is a highly general incremental computation/view maintenance technique that can maintain the output of an arbitrary and possibly recursive dataflow computation upon changes to its base inputs. As such, it is a promising technique for graph database management systems (GDBMS) that support continuous recursive queries over dynamic graphs. Although differential computation can be highly efficient for maintaining these queries, it can require a prohibitively large amount of memory. This paper studies how to reduce the memory overhead of DC with the goal of increasing the scalability of systems that adopt it. We propose a suite of optimizations that are based on dropping the differences of operators, both completely or partially, and recomputing these differences when necessary. We propose deterministic and probabilistic data structures to keep track of the dropped differences. Extensive experiments demonstrate that the optimizations can improve the scalability of a DC-based continuous query processor.
Energy Demand Unawareness and the Popularity of Bitcoin: Evidence from Nigeria

Decentralized cryptocurrency networks, notably those with high energy demand, have faced significant criticism and subsequent regulatory scrutiny. Despite these concerns, policy interventions targeting cryptocurrency operations in the pursuit of sustainability have largely been ineffective. Some were abandoned for fear of jeopardizing innovation, whereas others failed due to the highly globalized nature of blockchain systems. In search of a more effective angle for energy policy measures, this study adopts a consumer-centric perspective, examining the sentiments of Nigerian cryptocurrency users (${n=158}$) toward Bitcoin's sustainability, a representative cryptocurrency known for its high electricity demand. Three main findings emerged: 1) Even among those self-identifying as highly knowledgeable, most considerably underestimated Bitcoin's electricity consumption. 2) Participants with a more accurate understanding of Bitcoin's energy demand were more inclined to support sustainability measures. 3) Most of this supportive cohort viewed private entities as the primary stakeholders for implementing such measures. Given these findings, we suggest that consumer education should be at the forefront of policy initiatives aimed at cryptocurrency sustainability.
Applying Blockchain Technologies in Healthcare

Recently, one of the inventive developments penetrating many industries is blockchain technology. In the era of globalization and digitalization, blockchain has garnered interest in various application fields from health data management to clinical trials. In this study, we aimed to explore blockchain applications in healthcare with an explorative perspective with a scientometrics analysis. With this analysis, the trends and evolutionary relations between health and blockchain technology were examined via the queries in the Web of Science database. In the analysis, the author keyword co-occurrences were used for demonstrating concept relationships. To understand the new emerging study field, VosViewer was used for network visualizations and CiteSpace free java-based software was used for scientometrics analysis. As a result, it can be implied that the main focus areas of the studies on blockchain are solving payment systems, digital identity, and privacy and security issues in healthcare field.
From Raw Data to Data Standards through Quality Assessment and Semantic Annotation

Data quality and documentation are at the core of the FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al. 2016). Regarding biodiversity and more broadly ecology domains, complementary solutions of the well-known data standard (notably through Darwin Core (Wieczorek et al. 2012)) orientation are emerging from the intensive use of EML (Ecological Metadata Language (Michener et al. 1997)) metadata standard. These notably capitalize on using: semantic annotation from EML metadata documents that describe data attributes, and FAIR quality assessment as proposed by DataOne (Data Observation Network for Earth) network. semantic annotation from EML metadata documents that describe data attributes, and FAIR quality assessment as proposed by DataOne (Data Observation Network for Earth) network. Here we propose to present this point of view by orchestrating the production of rich (with attributes description and links with terminological resources terms) EML metadata from raw datafiles and, through the generation of FAIR metrics for direct assessment of FAIRness and creation of data standards like Darwin Core. Using EML, we can describe each data attribute (e.g., name, type, unit) and associate each attribute one to several terms coming from terminological resources. Using the Darwin Core vocabulary as a terminological resource, we can thus associate, on the metadata file, original attributes terms to corresponding Darwin Core ones. Then, the data and their metadata files can be processed in order to automatically create the necessary files for a Darwin Core Archive. By acting at the metadata level, associated with accessible raw data files, we can associate raw attribute names to standardized ones, and then, potentially create data standards.
Pre-embedding of small biopsies using a Paraform gel system in a high throughput pathology laboratory

In histology, the correct handling and orientation of small/thin biopsies is often crucial for diagnosis. Automation is progressively growing and modifying the routine work in the histopathology laboratories, providing new chances for quality improvement and workload optimization. We have tested the use of Paraform orientation gels together with an automated embedding system for processing small/thin biopsies, first skin, but also other tissue/organ biopsies. The study aimed to assess the benefits and challenges of routinely using orientation gels in a high throughput pathology laboratory. Gel introduction required a short training of the pathologists, including trainees, at grossing; it did not cause significant delay at grossing, interference with embedding, or microtome steps, whereas re-do inclusions and re-cut slides were significantly reduced. In conclusion, orientation gel and automatic embedding constituted an efficient system for small/thin biopsies that had to be correctly placed and orientated, allowing the re-modeling of technicians' workflow and very safe handling of small/thin biopsies that were not manipulated further after grossing.
A form error evaluation method of honeycomb core surface

Form error evaluation of the machined honeycomb surfaces has the power to deliver tremendous benefits to improve the machining accuracy and guide the assembly process. The line laser measurement can meet the high-resolution requirement for thin-walled honeycomb cell walls, but faces a high proportion of noise data consisting of burrs and outliers. In this paper, dimensionality reduction combined with random sample consensus or regression analysis is adopted to distinguish the irregular noise from the measurement data. A directional extraction of honeycomb core surface form based on locally weighted regression is proposed. A highly realistic honeycomb surface simulation method is established to solve the lack of honeycomb benchmarks. The simulation experiment shows that the accuracy of the proposed method is ±7 μm. The comparison experiment with coordinate measuring machine demonstrates the surface form RMS error differences for the polygonal and curved honeycomb by the two method are 7 μm and 19 μm, respectively.
TSCL: A time–space crossing location for side-channel leakage detection

Cryptographic algorithms on devices are vulnerable to the so-called side-channel attacks(SCAs). The security evaluation of the cryptographic circuits is necessary to provide earlier leakage detection before manufacture. However, the complexity of locating leakage points is high because of the long execution time and the large scale of the circuit. To overcome this limitation, we propose an efficient leakage location method named time–space crossing location(TSCL) to locate the leakage in the hardware design, which combines the dynamic and static analysis. Unlike the traditional detection methods, we analyze the design features and power consumption characteristics of implementation to construct the candidate set on dynamic detection while further verifying candidate leakage points in the static detection stage. The t -test is known as a dynamic detection method to decrease the time of formal verification based on label propagation. The design features decrease the complexity of dynamic detection while the results of power consumption analysis promote static detection. We confirm the efficiency of the proposed method on unprotected and masking implementation of AES. Moreover, TSCL can be regarded as a third-party tool in the existing EDA tools of hardware design.
Efficient Graph Isomorphism Query Processing using Degree Sequences and Color-Label Distributions

Given a set of data graphs and a query graph, graph isomorphism query processing is the problem of finding all the data graphs that are isomorphic to the query graph. Graph isomorphism query processing is a core problem in graph analysis of various application domains. In existing approaches, index construction or query processing takes much time as the graph sizes increase. In this paper, we propose an efficient algorithm for graph isomorphism query processing. We introduce the color-label distribution which represents the canonical coloring of a vertex-labeled graph. Based on degree sequences and color-label distributions, we introduce a two-level index, which helps us efficiently solve graph isomorphism query processing. Experimental results on real datasets show that the proposed algorithm is orders of magnitude faster than the state-of-the-art algorithms in terms of index construction time, and it runs faster than existing algorithms in terms of query processing time as the graph sizes increase.
Image defogging based on multi-input and multi-scale UNet

The coarse-to-fine image defogging strategy has been widely used in the structural design of individual image defogging networks. In the traditional method, multi-scale input image subnets are superimposed, so that the sharpness of the image is gradually improved from the bottom subnet to the top subnet, which inevitably leads to the loss of image details. Toward a fast and accurate dehazing network design, we revisit the coarse-to-fine strategy and present a multi-input and multi-scale U-Net (MIMS-UNet). The MIMS-UNet has two distinct features. On the one hand, the single-encoder of MIMS-UNet adopts multi-input and multi-scale image, which increases the computation amount but greatly improves the network performance. On the other hand, codec structures with context blocks are used to capture context information and recover more details. The experimental results show that the proposed method achieves good results in both quantification and visualization. Compared with the existing methods, the proposed network can achieve ideal results of defogging and effectively avoid color distortion after defogging.
Preliminary e-infrastructure for digital metrology

The digital transformation of the economy is the result of profound changes in science, technology and society. The response to these changes should be the latest version of the SI and the corresponding development of the new measurement standards. In this regard, an important issue for metrology is to eliminate barriers towards digital transformation. Digitization of metrological data is becoming especially relevant. In solving this problem, the development of the metrological e-infrastructure plays an important role. The paper is dedicated to the presentation and generalization of the results and the problem of creating an e-infrastructure. Based on the analysis performed a preliminary scheme for the e-infrastructure implementation in NMI has been presented. In particular, one of the most important issues of digital transformation – the implementation of DCC – is also associated with the development of e-infrastructure.