Reviewerly/exHarmony huggingface dataset:
Post-quantum ID-Based Ring Signatures from Symmetric-Key Primitives

Ring signatures and ID-based cryptography are considered promising in terms of application. A ring signature authenticates messages while the author of the message remains anonymous. ID-based cryptographic primitives suppress the need for certificates in public key infrastructures (PKI). In this work, we propose a generic construction for post-quantum ID-based ring signatures (IDRS) based on symmetric-key primitives from which we derive the first two constructions of IDRS. The first construction named $$\mathsf {PicRS}$$ utilizes the Picnic digital signature to ensure its security while the second construction $$\mathsf {XRS}$$ is motivated by the stateful digital signature XMSS instead of Picnic, allowing a signature size reduction. Both constructions have a competitive signature size when compared with state-of-the-art lattice-based IDRS. $$\mathsf {XRS}$$ can achieve a competitive signature size of 889 KB for a ring of 4096 users while the fully stateless $$\mathsf {PicRS}$$ achieves a signature size of 1.900 MB for a ring of 4096 users. In contrast, the shortest lattice-based IDRS achieves a signature size of 335 MB for the same ring size.
Detection of Bitcoin Miners by Clustering Crypto Address with Google BigQuery Open Dataset

Bitcoin blockchain is a financial transaction network and the bitcoin miners use pseudonymous crypto addresses. The miners use their hash rate or computational power to mine the next block. After successful mining, the miners get rewards and the block gets appended to the main canonical blockchain. These addresses are used by bitcoin miners to send the cryptocurrency from one address to another without revealing the true identity. We have used a clustering algorithm to find the ownership of these addresses. Google BigQuery dataset Crypto $$\_$$ bitcoin is used, and the data frame is got by running SQL query in Kaggle notebook. The results show four promising cluster aggregations with the appropriate centroids. The elbow method is used to determine the number of clusters, and the metric within-cluster sum of squares (WCSS) is also calculated. Thus, we can show that a simple K-means clustering algorithm can be used to detect the relationship between the addresses and miners. These clusters show the group of miners with similar computational power or hash rate. Thus, these pseudonymous crypto addresses can be linked to the bitcoin miners with a similar hash rate. The clusters are validated with Silhouette scores and the results are visualized.
Use the Spear as a Shield: An Adversarial Example Based Privacy-Preserving Technique Against Membership Inference Attacks

Recent researches demonstrate that machine learning models are vulnerable to privacy leakage attacks.Among them, the membership inference attack poses a serious threat to the privacy of confidential training data.In the membership inference attack, the adversary uses a membership inference model to determine whether a given data belongs to the training set of the target model based on the prediction of the target model.Few defenses have been proposed, but suffer from compromising the performance or quality of the target model, or cannot effectively resist against membership inference attacks.This paper proposes a novel adversarial example based privacy-preserving technique (AEPPT), which adds the crafted adversarial perturbations to the prediction of the target model to mislead the adversary's membership inference model.The added adversarial perturbations do not affect the accuracy of target model, but can prevent the adversary from inferring whether a specific data is in the training set of the target model.Since AEPPT only modifies the original output of the target model, the proposed method is general and does not require modifying or retraining the target model.Experimental results show that the proposed method can reduce the inference accuracy and precision of the membership inference model to 50%, which is close to a random guess.The recall of the membership inference model drops from 88.7% to 51.9% on the CIFAR100 dataset and drops from 98.5% to 17.1% on Purchase dataset.Moreover, the performances of the proposed method under various factors (i.e., perturbation step size, number of adversary's data, number of target model's output classes, and different membership inference models) are evaluated, which demonstrate that the proposed method can resist membership inference attacks under different factors.Further, for those adaptive attacks where the adversary knows the defense mechanism, the proposed AEPPT is also demonstrated to be effective.Compared with the state-of-the-art defense methods, e.g., the truncating method, the dropout method, and the adversarial regularization method, the proposed defense can significantly degrade the accuracy and precision of membership inference attacks to 50% (i.e., the same as a random guess).Meantime, the normal performance and utility of the target model will not be affected.
The Design of A Tethered Unmanned Aerial Vehicle (UAV)

research-article Share on The Design of A Tethered Unmanned Aerial Vehicle (UAV) Authors: HongHao Zheng School of Aerospace Engineering and Applied Mechanics, Tongji University, China School of Aerospace Engineering and Applied Mechanics, Tongji University, ChinaSearch about this author , Heng Yu School of Aerospace Engineering and Applied Mechanics, Tongji University, China School of Aerospace Engineering and Applied Mechanics, Tongji University, ChinaSearch about this author , YuanZhen Zhang School of Aerospace Engineering and Applied Mechanics, Tongji University, China School of Aerospace Engineering and Applied Mechanics, Tongji University, ChinaSearch about this author , Yao Lu School of Aerospace Engineering and Applied Mechanics, Tongji University, China School of Aerospace Engineering and Applied Mechanics, Tongji University, ChinaSearch about this author , TianYou Shao School of Aerospace Engineering and Applied Mechanics, Tongji University, China School of Aerospace Engineering and Applied Mechanics, Tongji University, ChinaSearch about this author , Mi Tang School of Aerospace Engineering and Applied Mechanics, Tongji University, China School of Aerospace Engineering and Applied Mechanics, Tongji University, ChinaSearch about this author , JinLong Zhu School of Aerospace Engineering and Applied Mechanics, Tongji University, China School of Aerospace Engineering and Applied Mechanics, Tongji University, ChinaSearch about this author Authors Info & Claims CMAAE 2021: 2021 International Conference on Mechanical, Aerospace and Automotive EngineeringDecember 2021 Pages 74–79https://doi.org/10.1145/3518781.3519237Online:07 June 2022Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Distributed HBase Cluster Storage Engine and Database Performance Optimization

With the development of network society, individuals and enterprises generate massive data every day. However, the traditional relational MySQL database has poor performance in storing and processing massive data. In the case of huge amount of data, MySQL may even crash, which obviously cannot meet the needs of massive data storage. Obviously, with more and more emphasis on user-friendliness, the storage and processing application of massive data determine the future development of enterprises. This paper discusses the storage engine, high concurrency, architecture and distributed cluster of HBase, and then compares the performance differences between MySQL and HBase optimized with Bloom filter(Bloom HBase) on Linux platform.
A Multiple-Blockchains based Service Monitoring Framework in Edge-Cloud Computing

In the edge-cloud computing, the applications usually are delivered as services, each of which runs independently and can cooperate to construct the complicated applications. However, it is difficult to monitor the services in the decentralized edge-cloud environment since there usually does not exist the central authorization to guarantee trustiness of the service quality data collected. To address the trust issue, the blockchain techniques have been introduced to the service monitoring. Considering the services usually are distributed on the edge nodes, if we deploy only a blockchain on the edge nodes near to the services, it may have to support both the service registration and monitoring because of the loose-coupling principle. That would influence the performance, even would crash the edge nodes. In this paper, we propose a multiple-blockchains based framework, including a registration blockchain that focuses on the service registration and discovery, and a set of monitoring blockchains that focus on the service invocation and monitoring. The registration blockchain is deployed on the cloud nodes, and the monitoring blockchains are deployed on the edge nodes near to the services for collecting efficiently and precisely the service quality data. Based on the cross-chain technology, we design a cross-chain service discovery between the monitoring blockchain and the registration blockchain for finding the services the requesters need. Finally, we implement the registration and monitoring blockchains based on the blockchain platform Cosmos and conduct a set of experiments to show the effectiveness of the proposed framework.
A Novel System for Image Text Recognition and Classification using Deep Learning

Nowadays, the recognition of patterns by artificial intelligence algorithms based on artificial neural networks is one of the broadest and most developed areas of study in the computer industry. With this study, a solution is proposed to the problem of recognizing handwritten textual information for statistical purposes based on the recognition of natural text by means of artificial neural networks. The system has been programmed to be able to acquire information from a scanner, digital camera, Pad or from a file, which gives it flexibility with potential users. This work presents the development of a computer system that allows the recognition of handwritten text based on image processing techniques, artificial intelligence, artificial neural networks, and pattern recognition for natural and handwritten text recognition systems. In the proposed system the handwritten text is recognized with precision of 0.81 or higher
BOBBLE: A Mixed Routing-Granularity Distributed Load Balancing for Data Center Networks

In current Datacenter Networks (DCNs), Equal-Cost Multipath (ECMP) is a default load balancing scheme. However, using ECMP may result in rapid growth of Flow Completion Time (FCT) due to its well-known drawbacks. In order to solve the problem of ECMP, load balancing schemes with per-packet granularity (such as RPS) and per-flowlet granularity (such as LetFlow) are proposed. These two different granularities have their own features, but there&#x0027;s no existing load balancing considering both their advantages. In this paper, we mix per-flowlet and per-packet in scheduling decisions and introduce the potential performance bottleneck in mixed granularities load balancing. Based on this idea, we propose a hybrid scheme for load balancing, referred to as BOBBLE. The key idea of BOBBLE is to distinguish elephant and mouse flows and schedule them with different granularity. We evaluate BOBBLE in simulations with NS-3. Experiment shows that BOBBLE slightly sacrifices elephant flows FCT but it is worthy because our results demonstrate that BOBBLE is a good trade-off which outperforms RPS and LetFlow in mouse flows FCT in both symmetric and asymmetric topology.
Technical note: A novel 3D scan-based optical method for analyzing lines drawn at different pen pressure

Pen pressure is one of the important elements of handwriting and questioned document analysis which is also crucial to recognize forgery. In this paper, we present a new, non-contact, non-destructive, and relatively inexpensive technique to measure the width of the grooves made by the writing. Results demonstrate a clear difference in measured groove widths for varying pen pressure. The effect of passing a written or signed paper in different types of printers has also been studied. A clear decrease of the groove depth was noted when passing the document through a laser printer. An ink jet printer only resulted in a small decrease of the groove depth, while no effect could be detected from a dot matrix printer.
Uncertain Integration and Composition Approach of Data from Heterogeneous WoT Health Services

In recent years, the usage of electronic health records (EHR), wearable devices, and health applications has expanded in popularity. Because of the abundance of data that has been accumulated and integrated, health self-management is becoming more practicable. Some of the difficulties that the current healthcare system is facing include smart homes and smart workplaces enabled by the internet of things. The Web of Things (WoT) is a subset of the Internet of Things that aims to connect everyday things to the Internet and manage interoperability. Furthermore, collaboration of health data with data from various devices at home and at work, as well as open data on the Internet, is critical for successful and accessible health self-management. Unfortunately, shared health data may be untrustworthy for a variety of reasons. Uncertainty can be caused by heterogeneity, incompleteness, unavailability, and data inconsistency. To address the problem of health data uncertainty, we provide a probabilistic approach for composing uncertain Health Connected Data and computing the probabilities to deliver the final degree of uncertainty. We also present a method for parsing typical WoT objects into a new programmatic form that mimics the uncertainty of health data. Using a health care use case, we show how our technique successfully integrates uncertain health data with home, work, and sport environment data for the WoT domain.
Managing Non-functional Requirements in Agile Software Development

Agile software development is an iterative software development methodology that aims at maximizing productivity, effectiveness, and speed of delivery. There are a lot of benefits of Agile Software Development. However, there are still some challenges. For example, non-functional requirements are not treated as first-class artifacts during the development lifecycle, which causes many problems such as customer dissatisfaction and much rework which therefore affects time and cost. This paper explains different solutions that have handled non-functional requirements issues in Agile. The paper shows the strength and weakness of each solution, however, there is no single solution that handles all main activities of requirements engineering such as elicitation, analysis, validation and management in regards of non-functional requirements.
Performance of Machine Learning Techniques in the Prevention of Financial Frauds

International Journal of Computer Sciences and Engineering (A UGC Approved and indexed with DOI, ICI and Approved, DPI Digital Library) is one of the leading and growing open access, peer-reviewed, monthly, and scientific research journal for scientists, engineers, research scholars, and academicians, which gains a foothold in Asia and opens to the world, aims to publish original, theoretical and practical advances in Computer Science,Information Technology, Engineering (Software, Mechanical, Civil, Electronics & Electrical), and all interdisciplinary streams of Computing Sciences. It intends to disseminate original, scientific, theoretical or applied research in the field of Computer Sciences and allied fields. It provides a platform for publishing results and research with a strong empirical component. It aims to bridge the significant gap between research and practice by promoting the publication of original, novel, industry-relevant research.
Metamorphic relations via relaxations: an approach to obtain oracles for action-policy testing

Testing is a promising way to gain trust in a learned action policy π, in particular if π is a neural network. A "bug" in this context constitutes undesirable or fatal policy behavior, e.g., satisfying a failure condition. But how do we distinguish whether such behavior is due to bad policy decisions, or whether it is actually unavoidable under the given circumstances? This requires knowledge about optimal solutions, which defeats the scalability of testing. Related problems occur in software testing when the correct program output is not known.
Review on Advanced Cost Effective Approach for Privacy with Dataset in Cloud Storage

Cloud computing allows customers to run compute and data-intensive applications without the need for a large investment in infrastructure. Additionally, a significant amount of intermediate datasets are created and often saved, in order to reduce the expense of re-computing these applications. It becomes difficult to protect the privacy of intermediate datasets because attackers may be able to retrieve information that is sensitive to privacy via the analysis of several intermediate datasets. Existing techniques to deal with this problem generally endorse the use of encryption for all cloud datasets. For data-intensive applications, the time and expense of repeatedly decrypting and encrypting intermediate datasets are prohibitive; hence, encrypting all intermediate datasets does not make sense. Big heterogeneous data storage concerns and challenges, countermeasures (security and administration) and cloud storage prospects, are discussed in this article. New questions arise for cloud storage researchers, when they examine these issues in depth.
Whatsapp Sentiment Analysis Using R

In today’s internet era, there are many chat application are used for conversions like whatsapp. Every smart phone user uses the Whatsapp application for private and group messaging, as well as video calling. Users join a variety of desired and undesirable WhatsApp groups, resulting in confusion, low storage capacity, and unmonitored issues. Sentiment analysis is progressively driving the design and delivery of more products and services that match customers' demands, from mining tweets to Facebook postings to scraping online reviews. It is somewhat easier to do nowadays, because of improved packages in popular programming languages. In this paper on Sentimental analysis of whatsapp data done by using R. This brings nature of the whatsapp group whether the conversion is positive or negative. Based on the result users can deliberate on those whatsapp group.
A Recommender System for Digital Newspaper Readers Based on Random Forest

In this research, the potential of machine learning methods based on decision trees (DT) and Random Forest (RF) models developed in the context of classifying readers of a digital newspaper. For this purpose, the number of visits of users to each section of the newspaper in a 3-month interval has been taken into account. The models of DT and RF developed in this paper classify the profiles of readers who access the journal with an accuracy of 98.07% and AUC value of 99.27%, thus demonstrating that it serves as a valid tool for making strategic and operational decisions when creating, manage and present content in the user – website interaction.
Holistic Network for Quantifying Uncertainties in Medical Images

Variability in delineation is an inherent property for segmenting medical imagery, when images are annotated by a variety of expert annotators. Previous methods have used adversarial training, Monte-Carlo sampling, and dropouts, which might sometimes produce a wide range of segmentation masks that differ from the styles of mask produced by a set of expert annotators. State-of-the-art method uses multiple U-Nets to capture the individual delineations, but it is computationally demanding. To mitigate this problem, a holistic network containing N-Encoder and N-Decoder is proposed, which could individually model the variability of delineation produced by the expert annotators. This will help to create segmentation masks for different tasks of the same dataset through a single network by learning the common features of multiple Encoders via a common channel and passing those features to Decoder. These create one segmentation mask. All the masks are calculated by using weighted loss at each end of the Decoders that show excellent results for some datasets.
Efficient Federated Tumor Segmentation via Normalized Tensor Aggregation and Client Pruning

Federated learning, which trains a generic model for different institutions without sharing their data, is a new trend to avoid training with centralized data, which is often impossible due to privacy issues. The Federated Tumor Segmentation (FeTS) Challenge 2021 has two tasks for participants. Task 1 aims at effective weight aggregation methods given a pre-defined segmentation algorithm for clients training. While task 2 looks for robust segmentation algorithms evaluated on unseen data from remote independent institutions. In federated learning, heterogeneity in the local clients’ datasets and training speeds results in non-negligible variations between clients in each aggregation round. The naive weighted average aggregation of such models causes objective inconsistency. As for task 1, we devise a tensor normalization approach to solve the objective inconsistency. Furthermore, we propose a client pruning strategy to alleviate the negative impact on the convergence time caused by the uneven training time among local clients. Our method achieves a projected convergence score of 74.32% during the training phase. For task 2, we dynamically adapt model weights at test time by minimizing the entropy loss to address the domain shifting problem for unseen data evaluation. Our method finally achieves dice scores of 90.67%, 86.23%, and 78.90% for the whole tumor, tumor core, and enhancing tumor, respectively, on the task’s validation data. Overall, the proposed solution ranked first for task 2 and third for task 1 in the FeTS Challenge 2021.
Multiaffine polynomials over a finite field

Abstract We consider polynomials f ( x 1 , …, x n ) over a finite field that possess the following property: for some element b of the field the set of solutions of the equation f ( x 1 , …, x n ) = b coincides with the set of solutions of some system of linear equations over this field. Such polynomials are said to be multiaffine with respect to the right-hand side b . We obtain the properties of multiaffine polynomials over a finite field. Then we show that checking the multiaffinity with respect to a given right-hand side may be done by an algorithm with polynomial (in terms of the number of variables and summands of the input polynomial) complexity. Besides that, we prove that in case of the positive decision a corresponding system of linear equations may be recovered with complexity which is also polynomial in terms of the same parameters.
Rule-Based Dialogue Systems: Architecture, Methods, and Tools

Until around 2000 dialogue systems developed in academic and industrial research laboratories were based on rules that determined the system’s behavior. Consider Example 2.1 in which the system has to choose between three different possible responses to the user’s utterance: U1: I want to book a flight to Boston. S1.1: Sorry, please repeat that. (System cannot interpret U1). S1.2: Did you say Boston? (System asks for confirmation). S1.3: Ok, a flight to Boston. What date? (System confirms implicitly and asks for the value of the next slot).