Reviewerly/exHarmony huggingface dataset:
Enhancing Privacy Protection in Intelligent Surveillance: Video Blockchain Solutions

Blockchain has emerged as a contemporary innovation that ensures secure operations in distributed networks, including decentralized applications, finance, logistics, and cross-border organizational control. In this paper, we introduce “Video Blockchain” as a novel method to store and manage visual data in smart cities, due to the lack of tamper resistance in existing systems. A relationship is established between video frames from surveillance videos and blockchain technology, integrating the visual data into a decentralized storage platform. A unique approach is leveraged to extract hash values and signatures from video blockchains using cryptographic functions, thereby enhancing surveillance data security. A decentralized blockchain prototype was developed, and appropriate cryptographic algorithms were selected to create a sustainable video blockchain. The contributions of this research project are to enhance blockchain security and minimize privacy-preserving gaps in intelligent surveillance, which lead to more secure, robust and reliable surveillance systems for smart cities.
Student Certificate Sharing System Using Blockchain and NFTs

In this paper, we propose a certificate sharing system based on blockchain that gives students authority and control over their academic certificates. Our strategy involves developing blockchain-based NFT certifications that can be shared with institutions or employers using blockchain addresses. Students may access the data created by each individual institute in a single platform, filter the view of the relevant courses according to their requirements, and mint their certificate metadata as NFTs. This method provides accountability of access, comprehensive records that are permanently maintained in IPFS, and verifiable provenance for creating, distributing, and accessing certificates. It also makes it possible to share certificates more safely and efficiently. By incorporating trust factors through data provenance, our system provides a countermeasure against issues such as fake and duplicate certificates. It addresses the challenge of the traditional certificate verification processes, which are lengthy manual process. With this system, students can manage and validate their academic credentials from multiple institutions in one location while ensuring authenticity and confidentiality using digital signatures and hashing for data protection against unauthorized access. Overall, our suggested system ensures data safety, accountability, and confidentiality while offering a novel approach to certificate distribution.
Performance Evaluation of Quantum-Resistant Cryptography on a Blockchain

Blockchain technologies are charting a promising trajectory, gaining increasing acceptance across various economic sectors due to their exceptional trust levels, underpinned by sophisticated encryption techniques. However, despite continuous advancements in the development of nearly indecipherable encryption methods, the rapid evolution of quantum computing capabilities poses a potential threat to these systems in the foreseeable future. In July 2022, the National Institute of Standards and Technology (NIST) introduced four quantum-resistant cryptographic algorithms, marking a significant milestone in this area. Nonetheless, with heightened security comes a potential performance drawback, especially when implemented in blockchain projects such as Ethereum. This study empirically demonstrates that the integration of the quantum-resistant cryptographic algorithm “Dilithium”—one of the “to be standardized candidates” announced by the NIST—significantly decelerates operation speeds within a distributed blockchain architecture when compared to the incumbent algorithms, ECC and RSA.
Report on Decentralized Identity Models and Study on Main Privacy Concerns

Decentralized Identity is a expanding topic regarding privacy matters which is strongly developed on blockchain technologies. It allows the users of the model to become the real and only owners of their data, being able of choosing how and with whom to share their information. Due to the growing interest in the topic, several proposals have been created, such as the Verifiable Credentials from the W3C, and recently the SoulBound Tokens from the Ethereum Foundation. The objective of this study is to show the impact on Web3.0 of both schemes, with important contributions to the ever-growing ecosystem. After said analysis, showed the importance of both paradigms due to their consolidated works and strong support by different communities, the necessity of a privacy analysis for their right use, mainly under the GDPR compliance, is highlighted. For the correct use and maintenance of both of them, a definition of their limits and most suitable use cases is made. To this end, the privacy concerns of both main models is studied, and a selection of the most appropriate use cases for each of them is proposed alongside based on their characteristics and necessities.
Learning with privileged and sensitive information: a gradient-boosting approach

We consider the problem of learning with sensitive features under the privileged information setting where the goal is to learn a classifier that uses features not available (or too sensitive to collect) at test/deployment time to learn a better model at training time. We focus on tree-based learners, specifically gradient-boosted decision trees for learning with privileged information. Our methods use privileged features as knowledge to guide the algorithm when learning from fully observed (usable) features. We derive the theory, empirically validate the effectiveness of our algorithms, and verify them on standard fairness metrics.
Navigating the Data Deluge: Challenges and Opportunities

This chapter provides a comprehensive exploration of the profound impact of the exponential growth of data in the twenty-first century. The first part of the chapter discusses the unprecedented increase in data volume and its implications for data management and processing. The massive influx of data demands efficient storage, processing, and analysis strategies to derive valuable insights. The chapter also emphasizes the evolving significance of data in the modern era, driving innovation and influencing decision-making processes across various industries and societal domains. Challenges with data are examined, including issues related to data security, privacy, quality, and the complexities of handling diverse data sources. Finding practical solutions to these challenges is critical to harnessing the full potential of the data deluge. Moreover, the chapter explores the emergence of a data economy at the global level, highlighting the strategic importance of data as a valuable resource for economic growth and development. Building a better data economy is proposed as a strategic goal, involving initiatives for data sharing, promoting data literacy, and establishing responsible data governance frameworks. Finally, the chapter highlights the pivotal role of policy strategies in shaping the data value chain. Policy measures for promoting data access, safeguarding data privacy, and encouraging data-driven innovation are discussed in detail.
Data Economy

The data economy, an intricate amalgamation of data and economic principles, has emerged as a transformative force in the modern world. Defined by the intricate interplay between data and economic activities, it encompasses a diverse range of sectors and stakeholders. This paradigm shift stems from the recognition that once a mere record, data has evolved into a prized resource, profoundly influencing business strategies, policy decisions, and societal progress. The data economy’s essence lies in its multifaceted components and stakeholders. It involves individuals, businesses, governments, data intermediaries, nonprofit organizations, academia, and society, forming a complex web of contributions and collaborations. However, this burgeoning landscape is challenging. Data privacy, security, access, interoperability, governance, and the digital divide emerge as significant obstacles, necessitating ethical guidelines, robust infrastructure, and a skilled workforce to navigate these complexities. The data economy’s impact is far-reaching, evident in its diverse types. The big data economy thrives on vast datasets and algorithmic analysis, revolutionizing industries, and decision-making. Personal data economy capitalizes on digital footprints to tailor experiences, while the industrial data economy optimizes processes through real-time insights. The algorithm economy ushers in automation and AI-led decision-making, albeit raising concerns of bias and job displacement. Amid this evolution, principles guide responsible data use: ownership, privacy, security, fairness, transparency, interoperability, innovation, and responsibility. These principles ensure equitable value distribution, mitigate risks, and drive innovation. The data economy’s significance is underscored by its role in fostering innovation, improving efficiency, enhancing customer experiences, job creation, and economic growth. It thrives on the relentless growth of data, which, by 2025, is projected to reach 175 zettabytes. As industries harness this wealth of information, the data economy acts as a cornerstone, propelling societies toward digital transformation, economic prosperity, and unparalleled innovation.
Federated statistical analysis: non-parametric testing and quantile estimation

The age of big data has fueled expectations for accelerating learning. The availability of large data sets enables researchers to achieve more powerful statistical analyses and enhances the reliability of conclusions, which can be based on a broad collection of subjects. Often such data sets can be assembled only with access to diverse sources; for example, medical research that combines data from multiple centers in a federated analysis. However these hopes must be balanced against data privacy concerns, which hinder sharing raw data among centers. Consequently, federated analyses typically resort to sharing data summaries from each center. The limitation to summaries carries the risk that it will impair the efficiency of statistical analysis procedures. In this work, we take a close look at the effects of federated analysis on two very basic problems, non-parametric comparison of two groups and quantile estimation to describe the corresponding distributions. We also propose a specific privacy-preserving data release policy for federated analysis with the K -anonymity criterion, which has been adopted by the Medical Informatics Platform of the European Human Brain Project. Our results show that, for our tasks, there is only a modest loss of statistical efficiency.
Anomaly Detection in a Combined Driving System based on Unsupervised Learning

Anomaly detection models using big data generated from facilities and equipment have been adopted for predictive maintenance in the manufacturing industry. When facility faults or defects occur, different patterns of abnormal data are shown owing to their component behaviors. By detecting these pattern changes, it is possible to determine whether a facility abnormality occurs. This study evaluated the anomaly detection results from a combined driving system consisting of three driving motors for about six months at a manufacturing site. The learning data with an autoencoder model for about a month at the beginning of vibration data collection and continuous monitoring of anomalies using reconstruction errors showed that a component defect occurred in one driving motor, and the reconstruction error increased progressively about three months earlier than a facility manager found the failure. In addition, the micro-electro-mechanical systems sensor showed high amplitude in the entire frequency domain when high reconstruction errors occurred. However, the integrated electronics piezoelectric sensor showed different patterns as high amplitude in a specific frequency domain. The results of this study will be helpful for detecting facility abnormalities in combined driving systems using vibration sensors.
Empirical Analysis of Disaggregated Cloud Memory on Memory Intensive Applications

Disaggregated Cloud Memory (DCM) is a hypervisor-based solution that allows client node to extend local memory by leveraging underutilized memory from remote node. These two nodes are generally connected through Remote Direct Memory Access (RDMA)-based high-bandwidth InfiniBand networks. DCM has been a viable alternative to mitigate the performance degradation of memoryintensive applications in memory-constrained environments. There has also been a growing interest in developing memory-intensive applications with managed languages (we call managed applications) such as Java and Python. These managed languages are easy to use but introduce unpredictability in memory usage at runtime. Despite the advantage of memory extension in DCM, the empirical studies that analyze the performance impact and overhead of running managed applications in DCM are lacking. This paper presents the results of a comprehensive study of DCM on both managed and unmanaged applications. The experimental results revealed that the performance degradation of unmanaged applications in DCM is only less than 6% due to fast remote paging and optimized page eviction policy. However, Garbage Collection (GC) severely degrades the performance of managed applications when page fault occurs, while DCM mitigates the performance degradation efficiently.
Air Canvas: Hand Tracking Using OpenCV and MediaPipe

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Mastering Complex Coordination Through Attention-Based Dynamic Graph

The coordination between agents in multi-agent systems has become a popular topic in many fields. To catch the inner relationship between agents, the graph structure is combined with existing methods and improves the results. But in large-scale tasks with numerous agents, an overly complex graph would lead to a boost in computational cost and a decline in performance. Here we present DAGMIX, a novel graph-based value factorization method. Instead of a complete graph, DAGMIX generates a dynamic graph at each time step during training, on which it realizes a more interpretable and effective combining process through the attention mechanism. Experiments show that DAGMIX significantly outperforms previous SOTA methods in large-scale scenarios, as well as achieving promising results on other tasks.
Recommendation Algorithm Based on Wide&amp;Deep and FM

Online learning is more and more popular because it is not limited by time and space. How to choose a suitable course from thousands of online courses is a great challenge faced by online learners, and online course recommendation came into being. The personalized recommendation algorithm analyzes the user’s preferences by collecting some previous historical records of the user and other information, and generates recommendations for the user. Since Wide&Deep was proposed, due to its inherent ease of implementation, adaptability, and versatility, this approach has gained significant traction across various industry sectors. But its feature intersection method is not efficient. Sufficient feature engineering is required to provide informative features that can effectively distinguish objects. In this paper, the WD-FM model is proposed by combining Wide&Deep and factorization machine, and good results have been achieved through experimental demonstration.
Introducing DeReKoGram: A Novel Frequency Dataset with Lemma and Part-of-Speech Information for German

We introduce DeReKoGram, a novel frequency dataset containing lemma and part-of-speech (POS) information for 1-, 2-, and 3-grams from the German Reference Corpus. The dataset contains information based on a corpus of 43.2 billion tokens and is divided into 16 parts based on 16 corpus folds. We describe how the dataset was created and structured. By evaluating the distribution over the 16 folds, we show that it is possible to work with a subset of the folds in many use cases (e.g., to save computational resources). In a case study, we investigate the growth of vocabulary (as well as the number of hapax legomena) as an increasing number of folds are included in the analysis. We cross-combine this with the various cleaning stages of the dataset. We also give some guidance in the form of Python, R, and Stata markdown scripts on how to work with the resource.
Lost in Transformation: Rediscovering LLM-Generated Campaigns in Social Media

This paper addresses new challenges of detecting campaigns in social media, which emerged with the rise of Large Language Models (LLMs). LLMs particularly challenge algorithms focused on the temporal analysis of topical clusters. Simple similarity measures can no longer capture and map campaigns that were previously broadly similar in content. Herein, we analyze whether the classification of messages over time can be profitably used to rediscover poorly detectable campaigns at the content level. Thus, we evaluate classical classifiers and a new method based on siamese neural networks. Our results show that campaigns can be detected despite the limited reliability of the classifiers as long as they are based on a large amount of simultaneously spread artificial content.
Trajectory Optimization Control System of Intelligent Robot Based on Improved Particle Swarm Optimization Algorithm

Trajectory optimization is a hot topic in the field of intelligent robots, whose task is to plan the optimal motion trajectory that passes through a specified point and satisfies constraints such as velocity and acceleration based on a given target trajectory point. In the time optimization problem of robots, particle swarm optimization (PSO) has been widely applied in the time optimization problem of robotic arms due to its simple structure and adjustable parameters. This article conducts research on the intelligent robot trajectory optimization control system based on PSO, and the results show that through experimental data of three joints, it can be found that the optimized trajectory motion time has been reduced by an average of about 50%, achieving the expected goal. Compared with before optimization, PSO has stronger global search ability, faster convergence speed, and good stability, which can effectively improve robot work efficiency and maintain smooth operation. By dynamically adjusting the value of the learning factor in the particle swarm optimization algorithm through PSO, the particle swarm can search for the optimal value in a short period of time in the early stage of iteration and can quickly and accurately converge to the optimal solution in the later stage of iteration.
A Survey of Target Orientation Detection Algorithms Based on GPU Parallel Computing

The ocean has rich natural resources and irreplaceable strategic role. The urgent demand for ocean exploration has promoted the rapid development of underwater acoustic engineering. Beamforming has the capability of spatial filtering, is also an important means of target bearing estimation. Beamforming is the key technology of underwater acoustic system now, and it is also one of the most computationally intensive parts in underwater acoustic signal processing. In recent years, the signal type of underwater acoustic detection has transited from narrow-band signal to wide-band signal which can carry more information, which further increases the calculation amount of beamforming, and the system has higher requirements for the speed of signal processing. Therefore, the application of parallel processing technology based on graphics processor platform to the acceleration of underwater acoustic array signals will have significant practical value.
PENGARUH SERTIFIKASI HALAL, ENDORSEMET, DAN KUALITAS PRODUK TERHADAPKEPUTUSAN PEMBELIAN IPRODUK KOSMETIK WARDAH DI KOTA MATARAM

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Data mining algorithm based on Renyi fuzzy association rule: an application for the selection of suitable course

Data mining has been used to discover patterns in the data. It is a knowledge discovery technique to analyze hidden pattern and information from the available database. Due to uncertainty, fuzzy-based data mining techniques have been incorporated for decision-making. Fuzzy based data mining techniques model incomplete information and relations effectively in situations where lack of reasoning is given. A new fuzzy data mining algorithm has been proposed that helps in decision-making under uncertain conditions. The demonstration of the algorithm has been supported by means of a case study for the selection of suitable careers after studies. In the case study, data of 60 students studying in 4 different universities, pursuing 3 different courses have been taken. The decision making for the preparatory classes such as, further studies (F-S) or for job-oriented (JO) or coaching has been done by using the proposed method. From the results, it is concluded that student studying in the university U2 pursuing course C2 will take preparatory classes for job oriented course.
Simulating the Software Development Lifecycle: The Waterfall Model

This study employs a simulation-based approach, adapting the waterfall model, to provide estimates for software project and individual phase completion times. Additionally, it pinpoints potential efficiency issues stemming from suboptimal resource levels. We implement our software development lifecycle simulation using SimPy, a Python discrete-event simulation framework. Our model is executed within the context of a software house on 100 projects of varying sizes examining two scenarios. The first provides insight based on an initial set of resources, which reveals the presence of resource bottlenecks, particularly a shortage of programmers for the implementation phase. The second scenario uses a level of resources that would achieve zero-wait time, identified using a stepwise algorithm. The findings illustrate the advantage of using simulations as a safe and effective way to experiment and plan for software development projects. Such simulations allow those managing software development projects to make accurate, evidence-based projections as to phase and project completion times as well as explore the interplay with resources.