Extra Data Research Articles

Vision Permutator: A Permutable MLP-Like Architecture for Visual Recognition.

In this paper, we present Vision Permutator, a conceptually simple and data efficient MLP-like architecture for visual recognition. By realizing the importance of the positional information carried by 2D feature representations, unlike recent MLP-like models that encode the spatial information along the flattened spatial dimensions, Vision Permutator separately encodes the feature representations along the height and width dimensions with linear projections. This allows Vision Permutator to capture long-range dependencies and meanwhile avoid the attention building process in transformers. The outputs are then aggregated in a mutually complementing manner to form expressive representations. We show that our Vision Permutators are formidable competitors to convolutional neural networks (CNNs) and vision transformers. Without the dependence on spatial convolutions or attention mechanisms, Vision Permutator achieves 81.5% top-1 accuracy on ImageNet without extra large-scale training data (e.g., ImageNet-22k) using only 25M learnable parameters, which is much better than most CNNs and vision transformers under the same model size constraint. When scaling up to 88M, it attains 83.2% top-1 accuracy, greatly improving the performance of recent state-of-the-art MLP-like networks for visual recognition. We hope this work could encourage research on rethinking the way of encoding spatial information and facilitate the development of MLP-like models. PyTorch/MindSpore/Jittor code is available at https://github.com/Andrew-Qibin/VisionPermutator.

Experimental Evaluation and Analysis of Federated Learning in Edge Computing Environments

Federated learning (FL) is a machine learning system that allows a network of devices to train a model without centralized data. This characteristic makes FL an ideal choice for machine or deep learning using user data while maintaining privacy. Many applications, such as autonomous vehicles that adapt to pedestrian behavior and smart healthcare devices, require machine learning to be performed in edge computing environments. However, the performance of FL in edge computing environments has received limited research attention, and previous studies have not comprehensively evaluated heterogeneity aspects, such as system heterogeneity, statistical heterogeneity, and communication bandwidth. This study aims to fill this research gap by conducting experimental evaluations and detailed analyses of FL in edge computing environments. Specifically, we set up an experimental testbed based on the FL framework on the KubeEdge platform and evaluate the diverse effects of heterogeneity aspects on the model convergence time such as homogeneous versus heterogeneous hardware resources, balanced versus imbalanced data distributions, duplicate versus nonduplicate datasets, client participation ratio at each round, the client selection algorithm, and the communication bandwidth. Overall, this study contributes to the advancement of the field of FL by expanding the understanding of its performance in edge computing environments and providing guidelines for efficient and effective FL systems in heterogeneous environments, which can ultimately benefit various industries and domains.

Perancangan dan Implementasi Sistem Informasi Gereja Kristen Jawa Plengkung Berbasis Web Menggunakan Framework CodeIgniter

Information system is one of the thing that can be called as mandatory to be had by an institution right now. Gereja Kristen Jawa Plengkung (GKJP) is still using all of its record by traditional way. Data saved by tradisional way can caused a duplication of data called redundancy. With the existence of information system, activities in GKJP like data saving are expected to go well for the future. In the making of information system, waterfall is used as the research method for the development of information system. Waterfall usually has steps like (1) analytic, (2) design, (3) coding, (4) testing, (5) support. Based on the research and the testing that has been done, design and implementation of information system in Gereja Kristen Jawa Plengkung with Web as its base and the using of CodeIgniter Framework can be called success because it can fulfill the requirement suggested by the user.

Research on cloud storage biological data deduplication method based on Simhash algorithm

Research on cloud storage biological data deduplication method based on Simhash algorithm

A Data Deduplication Scheme Based on DBSCAN With Tolerable Clustering Deviation

To protect data privacy, users prefer to store encrypted data in cloud servers. Cloud servers reduce the cost of storage and network bandwidth by eliminating duplicate copies. To address the potential internal data leakage problem, the concept of clustering deviation is proposed for the first time. We improve the DBSCAN algorithm to tolerate clustering deviation. A data deduplication scheme is built upon the new algorithm, which considers users as clustering samples. Instead of immediately re-clustering new users, a certain deviation is tolerated to assign the users to the existing classes. We determine the popularity of the data according to user clustering results and apply different encryption schemes to protect the security of unpopular data more effectively. The performance of the algorithm is analyzed and compared with other methods through experiments, and the results verify the feasibility and efficiency of the proposed deduplication scheme.

FSACE: finite state automata-based client-side encryption for secure data deduplication in cloud computing

FSACE: finite state automata-based client-side encryption for secure data deduplication in cloud computing

De-duplication avoidance in regional names using an approach based on pronunciation

Demographic data deduplication has occurred in every field, including government, marketing, and opinion research, particularly if you work in IT and are in charge of taking backups or transferring large amounts of data. Duplication occurs both directly and indirectly when copying the same backup. As a result, there is an inherent need to proceed or remove redundant data. The term

FSACE: finite state automata-based client-side encryption for secure data deduplication in cloud computing

FSACE: finite state automata-based client-side encryption for secure data deduplication in cloud computing

Improved cloud storage auditing scheme with deduplication.

Cloud storage has become a crucial service for many users who deal with big data. The auditing scheme for cloud storage is a mechanism that checks the integrity of outsourced data. Cloud storage deduplication is a technique that helps cloud service providers save on storage costs by storing only one copy of a file when multiple users outsource the same file to cloud servers. However, combining storage auditing and deduplication techniques can be challenging. To address this challenge, in 2019 Hou et al. proposed a cloud storage auditing scheme with deduplication that supports different security levels of data popularity. This proposal is interesting and has practical applications. However, in this paper, we show that their proposal has a flaw: the cloud or other adversaries can easily forge the data block's authenticators, which means the cloud can delete all the outsourced encrypted data blocks but still provide correct storage proof for the third-party auditor. Based on Hou et al.'s scheme, we propose an improved cloud storage auditing scheme with deduplication and analyze its security. The results show that the proposed scheme is more secure.

Health Data Deduplication Using Window Chunking-Signature Encryption in Cloud

Due to the development of technology in medicine, millions of health-related data such as scanning the images are generated. It is a great challenge to store the data and handle a massive volume of data. Healthcare data is stored in the cloud-fog storage environments. This cloud-Fog based health model allows the users to get health-related data from different sources, and duplicated information is also available in the background. Therefore, it requires an additional storage area, increase in data acquisition time, and insecure data replication in the environment. This paper is proposed to eliminate the de-duplication data using a window size chunking algorithm with a biased sampling-based bloom filter and provide the health data security using the Advanced Signature-Based Encryption (ASE) algorithm in the Fog-Cloud Environment (WCA-BF + ASE). This WCA-BF + ASE eliminates the duplicate copy of the data and minimizes its storage space and maintenance cost. The data is also stored in an efficient and in a highly secured manner. The security level in the cloud storage environment Windows Chunking Algorithm (WSCA) has got 86.5%, two thresholds two divisors (TTTD) 80%, Ordinal in Python (ORD) 84.4%, Boom Filter (BF) 82%, and the proposed work has got better security storage of 97%. And also, after applying the de-duplication process, the proposed method WCA-BF + ASE has required only less storage space for various file sizes of 10 KB for 200, 400 MB has taken only 22 KB, and 600 MB has required 35 KB, 800 MB has consumed only 38 KB, 1000 MB has taken 40 KB of storage spaces.

The Quest for Omnioculars: Embedded Visualization for Augmenting Basketball Game Viewing Experiences.

Sports game data is becoming increasingly complex, often consisting of multivariate data such as player performance stats, historical team records, and athletes' positional tracking information. While numerous visual analytics systems have been developed for sports analysts to derive insights, few tools target fans to improve their understanding and engagement of sports data during live games. By presenting extra data in the actual game views, embedded visualization has the potential to enhance fans' game-viewing experience. However, little is known about how to design such kinds of visualizations embedded into live games. In this work, we present a user-centered design study of developing interactive embedded visualizations for basketball fans to improve their live game-watching experiences. We first conducted a formative study to characterize basketball fans' in-game analysis behaviors and tasks. Based on our findings, we propose a design framework to inform the design of embedded visualizations based on specific data-seeking contexts. Following the design framework, we present five novel embedded visualization designs targeting five representative contexts identified by the fans, including shooting, offense, defense, player evaluation, and team comparison. We then developed Omnioculars, an interactive basketball game-viewing prototype that features the proposed embedded visualizations for fans' in-game data analysis. We evaluated Omnioculars in a simulated basketball game with basketball fans. The study results suggest that our design supports personalized in-game data analysis and enhances game understanding and engagement.

Cross-Mix Monitoring for Medical Image Segmentation With Limited Supervision

Image segmentation is a fundamental building block of automatic medical applications. It has been greatly improved since the emergence of deep neural networks. However, deep-learning-based models often require a large amount of manual annotations, which has seriously hindered their practical usage. To alleviate this problem, numerous works were proposed by utilizing unlabeled data based on semi-supervised frameworks. Recently, the Mean-Teacher (MT) model has been successfully applied in many scenarios due to its effective learning strategy. Nevertheless, the existing MT model still has certain limitations. Firstly, to gain extra generalization ability through consistency training, various sorts of perturbations are often added to the training data. However, if the variation is too weak, it may cause the Lazy Student Phenomenon, and cause fluctuations in the learning model. On the contrary, large image perturbations may enlarge the performance gap between the teacher and student. In this case, the student may lose its learning momentum, and more seriously, drag down the overall performance of the whole system. In order to address these issues, we introduce a novel semi-supervised medical image segmentation framework, in which a Cross-Mix Teaching paradigm is proposed to provide extra data flexibility, thus effectively avoid Lazy Student Phenomenon. Moreover, a lightweight Transductive Monitor is applied to serve as the bridge that connects the teacher and student for active knowledge distillation. In the light of this cross-network information mixing and transfer mechanism, our method is able to continuously explore the discriminative information contained in unlabeled data. Extensive experiments on challenging medical image data sets demonstrate that our method outperforms current state-of-the-art semi-supervised segmentation methods under severe lack of supervision.

Optimized Proactive Recovery in Erasure-Coded Cloud Storage Systems

Cloud data centers have started utilizing erasure coding in large-scale storage systems to ensure high reliability with limited overhead compared to replication. However, data recovery in erasure coding incurs high network bandwidth consumption compared to replication. Cloud storage systems also play an important role in the energy consumption of data centers. Heuristic proactive recovery algorithms select all data blocks from failure-predicted disk/machine and perform proactive replication that contributes to huge recovery bandwidth savings. However, they fail to optimize the selection. Optimization can further improve resource savings. To address this issue, we propose a recovery algorithm that applies minimization on data blocks selected for proactive replication by considering the necessary and appropriate constraints that are constructed based on the system’s current network traffic and data duplication information. We evaluate the proposed algorithm using extensive simulations. Experiments show that the recovery algorithm reduces network traffic by 60% and storage overhead by 46% compared to the heuristic proactive recovery approach. Also, the proposed proactive recovery methods reduce the storage system’s energy consumption by up to 52% compared to replication.

Designing dynamic adjustment approach of event reliability in wireless sensor network

Many industries, such as environmental monitoring, industrial automation, healthcare, agriculture, and more, use wireless sensor networks. A critical requirement for wireless sensor networks (WSNs) is reliable transmission from source to destination. The main challenges for reliable data transmission are network connectivity, coverage optimization, data aggregation, shortest path, congestion, bandwidth, and quality of service. In a static environment, all sensor units transmit maximum data to the sink, dramatically raising their energy utilization and resulting in their early death. A dynamic environment can be utilized to collect data to reduce the amount of energy required to transport lengthy data packets over a wide area while still maintaining sensor node energy usage. The dynamic environment and duplicate packet simulation reflect real-world challenges like changing node positions and the potential for data duplication due to wireless communication issues. This paper introduces a dynamic event reliability protocol (DERP) in which the new positions of the sensor nodes are generated randomly in each event and a mobile sink collects data from every sensor node. The proposed technique consists of four parts, including a network model, a radio and energy model, a proposed approach, and a performance analysis. Our results show that DERP significantly improves energy consumption and unique packet delivery while reducing packet loss and the number of duplicate packets against LEACH and the average k-means clustering approach.

Using Blockchain Technologies to Manage Business in the Digital Economy

Blockchain is a distributed data storage technology that includes information about all transactions of system participants in the form of a blockchain. Blockchain offers a reliable way to perform processes, ensures stable performance, eliminates the possibility of data duplication, and reduces costs. The promising technology is rapidly gaining popularity and is being implemented in various fields, including business process management. However, even though blockchain technology has been around for several years, many business leaders are still not familiar with it because there is very little information and blockchain technology has not yet been widely used in enterprise infrastructure. Therefore, blockchain technology for business process management remains a hot topic, and the significant advantages of blockchain technology in improving business efficiency cannot be ignored. Thus, this article discusses the main features of using blockchain technology in the management of business processes in the digital economy. Also, the main advantages and disadvantages of technology implementation are presented, problems that business leaders may face are considered. In addition, the important aspects of the use of blockchain technology in the management of business processes in the digital economy are analyzed.

Rancang Bangun Sistem Informasi Bimbingan Online (SIMABIN) dengan Fitur Whatsapp Gateway Notifications

The Academic Manager of STMIK IKMI Cirebon does not know the status of theses and final assignments submitted by students for the 2020/2021 academic year. They do not know who has or has not completed the mentoring procedure until a mentoring report is issued at the end of the mentoring period. The process is done offline and each process involves manual labor, paper-based guidance cards, duplicate, invalid, or empty data are examples of data restrictions. It can take a while to search for data, missing files, or missing files through physically stacked folders. An integrated web-based information system is the solution. Users can access it from any platform, including supervisors, BAAK, heads of study programs, and students. The research method using UML (Unified Modeling Language) and Extreme Programming (XP). The results showed that White Box Testing was 85% in the Online Guidance Information System (SIMABIN) for the main module so it was feasible for the deployment process. The application has gone through the deployment process and can record all student guidance activities starting from the process of submitting titles, guidance requirements, assignment of supervisors, process of guidance sessions, to the eligibility of hearings for each guidance week. The application can be accessed at https://ikmiapp.web.id/simabin.

Evaluasi Implementasi Prepopulated Error System Pada Aplikasi Efaktur 3.0

The modernization program gave by the Directorate General of Taxes (DGT) in further developing the assistance nature of PKP citizens in the tax assessment area through guiding the pre-populated 3.0 e-faktur application is thought of as viable and adequate to citizens. The consequences of past investigations discovered that steering this application can lessen contributing mistakes on input charge solicitations physically, make it simpler to report VAT period expense forms, to work on the nature of administrations did by the Directorate General of Taxes. Be that as it may, with the expanding nature of administration for citizens, steering this application additionally needs to assess the issues and requirements experienced by citizens in showing and working this application to taking a stock of pre-populated VAT Tax Returns (SPT) issues and approving Import Notifications of Goods ( PIB). Does this application framework actually must be updated in running and working it (Error System), and what are the answers for manage issues and hindrances in case of a framework mistake that is frequently capable by citizens in working this application. The motivation behind this study is to make it simpler for citizens to deal with issues and impediments in the blunder framework application, assess the execution of pre-populated VAT returns subsequent to running the pre-populated e-receipt 3.0 application. This examination strategy utilizes subjective techniques with information assortment cycles and specialized information investigation. Information examination information sources are acquired from information assortment through e-research, subjective methodologies through meetings and perceptions. The aftereffects of the discoveries of this study are relied upon to give direction on the straightforwardness and perfection of the Directorate General of Taxes (DGT) in expanding the utilization of this pre-populated e-receipt 3.0. It is likewise an extra knowledge into data for scientists, instructors/speakers and citizens. Turn into a reference and extra data for the Directorate General of Taxes in working on the nature of assessment administrations, as well just like a wellspring of guidance and contribution for strategy producers. Subsequently, the creator raises research on e-invoicing applications that frequently experience framework blunders in their activities.

ANALYSIS OF THE EFFICIENCY OF DATA CHUNKING METHODS FOR DATA DEDUBLICATION SYSTEMS

There is a significant increase in the amount of data that needs to be stored worldwide. More and more companies are turning their attention to deduplication systems, which effectively increase data warehouse volume and reduce storage costs. Deduplication not only reduces the overall amount of information in storage but also reduces the load on networks by eliminating the need to retransmit duplicate data. In this work, we considered the stages that any deduplication system includes, namely chunking, hashing and indexing, mapping. The effectiveness of deduplication systems primarily depends on the choice of the method of dividing the data stream at the chunking stage. We considered the classic Two Threshold Two Divisor (TTTD) method, which is widely used in modern deduplication systems. This method uses Rabin’s fingerprint to find the hash of the substring value. The formula for calculating the hash for the first substring and the formula for calculating the rest of the substring are given. Another method we investigated is Content Based Two Threshold Two Divisor (CB-TTTD) – it uses new hash functions to fragment the data stream, and the corresponding formulas for calculating the first and each subsequent substring are given. To test the effectiveness of these two methods, we developed a test deduplication system, implemented these two fragmentation methods, and tested their performance on two sets of text data. We have modified these methods with the addition of a new string-splitting condition based on the content specification of the data we tested. The results of a comparison of the work of classical and modified methods are given. Using metrics to compare the efficiency of data fragmentation methods, we obtained experimental data, based on which we can make conclusions about the feasibility of using CB-TTTD as an alternative to TTTD in new deduplication systems. The obtained data can be used in the development of new highly efficient data deduplication systems and to improve old solutions

Chaotic-based Orthogonal Frequency Division Multiplexing with Index Modulation

Orthogonal frequency division multiplexing with index modulation (OFDM-IM), stands out among conventional communication technologies, as it uses the indices of the available transmit entities. Thanks to such an approach, it offers a novel method for the transmission of extra data bits. Recent years have seen a great interest in chaos-based communications. The spectrum-spreading signals used in chaotic signal modulation technologies are orthogonal to the existing mixed signals. This paper presents how well a non-coherent differential chaos shift keying communication system performs across an AWGN. Different types of detection methods are simulated, bit error rate and power spectral density are calculated and then compared with standard OFDM with index modulation. The results of simulations concerning the performance of a DCSK system, adding to the security of the proposed solution and offering a comparable bit error rate performance, are presented in the paper as well.

Excessive and asymmetrical removal of heterozygous sites by maxSH biases downstream population genetic inference: Implications for hybridization between two primroses

AbstractTechniques of reduced‐representation sequencing (RRS) have revolutionized ecological and evolutionary genomics studies. Precise establishment of orthologs is a critical challenge for RRS, especially when a reference genome is absent. The proportion of shared heterozygous sites across samples is an alternative criterion for filtering paralogs. In the prevailing pipeline for variant calling of RRS data – PYRAD/IPYRAD, maxSH is an often overlooked parameter with implications to detecting and filtering paralogs according to shared heterozygosity. Using empirical genotyping by sequencing data of two primroses (Primula alpicola Stapf and Primula florindae Ward) and their putative hybrids, and extra data sets of Californian golden cup oaks, we explore the impact of maxSH on filtering paralogs and further downstream analyses. Our study sheds light on the simultaneous validity and risk of filtering paralogs using maxSH, and its significant effects on downstream analyses of outlier detection, population assignment, and demographic modeling, emphasizing the importance of attention to detail during bioinformatic processes. The mutual confirmation between results of population assignment and demographic modeling in this study suggested maxSH = 0.10 has a potentially excessive and asymmetrical effect on the removal of truly shared heterozygous sites as paralogs. These results indicate that hybridization origin hypotheses of putative hybrids represented by results with maxSH = 0.25 and 0.50 are more credible. In conclusion, we revealed the critical hazard of paralogs filtration according to sharing heterozygosity at first, so that we propose to use specific protocols, rather than maxSH, to filter potential paralogs for closely related lineages.