Reviewerly/exHarmony huggingface dataset:
Comparison of personal information de-identification policies and laws within the EU, the US, Japan, and South Korea

In the era of big data, data creates new added value by collecting, analyzing, and transforming the thoughts and actions of economic members and creating insights that can predict the future. This means that in the near future, data-driven decisions based on data, rather than subjectivity or experience, will be the driving force behind society. However, since valuable and useful data is bound to be generated from personal information, how to safely utilize personal information is becoming an important topic in the big data era. To protect personal information in this environment, data services and database providers have increased their focus on the implementation of de-identification, a technique that can protect personal information while maintaining the usefulness of the data. Moreover, many countries have introduced new policies and laws focusing on the de-identification of personal information. Accordingly, this paper compares and analyzes how the European Union, the United States, Japan, and South Korea have recently adopted the concept of de-identification in their own personal information protection laws, and presents common trends and implications. As a comparative framework, each country's conceptual classification system related to de-identification, legal treatment, data controller obligations, and de-identification procedures was included. This study identifies the shifts made in each country's regulatory system following the introduction of the concept of de-identification. These include a shift from a binary approach to an approach that considers the identifiability spectrum, from a belief in anonymization to regulation from a risk management perspective, and from a focus on de-identification methods, to responsibility for follow-up management. This study contributes to the establishment of specialized knowledge of de-identification practices by empirically examining the current status of de-identification information-related legal systems adopted by major countries/regions. Also, the study proved the actualization of theory by confirming that the de-identification policy approach from the perspective of risk management is actually applied to the laws of each country. In addition, the attempt to present a framework for systematic comparison of de-identification systems by country provides a new perspective that can trace the trend of future de-identification system changes on a consistent basis. In addition, this study brings the gradual expansion of data policy research by expanding the research on de-identified information, which has been studied mainly in Europe and the United States, to case studies in Japan and South Korea.
Artifacts in Localised Multivariate Uniform Conditioning: A Case Study

Abstract Localised Multivariate Uniform Conditioning (LMUC) is a technique designed for spatially locating Selective Mining Unit (SMU) grades derived using Multivariate Uniform Conditioning (MUC) for the assessment of recoverable resources. LMUC has the advantage of producing SMU estimates conforming to the MUC panel-specific grade-tonnage curves while preserving the spatial grade distribution at the selective mining level. However, LMUC results have two severe artifacts. This paper documents both artifacts using four grades from a large nickel–cobalt laterite deposit in Western Australia.
Applying Clustering Techniques and Geostatistics to the Definition of Domains for Modelling

Abstract Machine learning is a broad field of study that can be applied in many areas of science. In mining, it has already been used in many cases, for example, in the mineral sorting process, in resource modeling, and for the prediction of metallurgical variables. In this paper, we use for defining estimation domains, which is one of the first and most important steps to be taken in the entire modeling process. In unsupervised learning, cluster analysis can provide some interesting solutions for dealing with the stationarity in defining domains. However, choosing the most appropriate technique and validating the results can be challenging when performing cluster analysis because there are no predefined labels for reference. Several methods must be used simultaneously to make the conclusions more reliable. When applying cluster analysis to the modeling of mineral resources, geological information is crucial and must also be used to validate the results. Mining is a dynamic activity, and new information is constantly added to the database. Repeating the whole clustering process each time new samples are collected would be impractical, so we propose using supervised learning algorithms for the automatic classification of new samples. As an illustration, a dataset from a phosphate and titanium deposit is used to demonstrate the proposed workflow. Automating methods and procedures can significantly increase the reproducibility of the modeling process, an essential condition in evaluating mineral resources, especially for auditing purposes. However, although very effective in the decision-making process, the methods herein presented are not yet fully automated, requiring prior knowledge and good judgment.
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.
Review of Movie Recommender Systems Based on Deep Learning

With the development of the network, society has moved into the data era, and the amount of data is exploding, we need a tool to help users find corresponding data collections based on their interests, and recommender systems were born for this purpose. In the movie field, recommender systems suggest items that users may like, improving the efficiency of finding movies and optimizing the user experience thus driving the growth of the movie industry. Machine learning is a multi-disciplinary science that focuses on how to improve the performance of algorithms by continuously reorganizing existing knowledge structures in a way that mimics human learning. Deep learning is a research direction in the field of machine learning that has achieved results in many areas that far surpass previous related techniques. In order to better provide personalized services to users and improve the accuracy of the system’s recommendations, it is necessary to integrate deep learning techniques into the recommender system to optimize the system’s performance. In this paper, we review different approaches in deep learning based recommender systems.
Correlational Statistics

Abstract This chapter explains how correlational statistics are used to summarize and describe associations between variables. Music teachers are often curious about how various phenomena relevant to music teaching and learning may be related to each other. Although identifying correlations among variables can yield important insights, it is important to remember that correlation does not equal causation. That is, just because two variables are related does not mean that one necessarily caused the other. Much can be learned about the nature of a relationship between interval- and/or ratio-level variables by creating a scatterplot. The most common statistical tool used for investigating correlation (i.e., the direction and degree of a relationship) between two interval- or ratio-level variables is the Pearson Product Moment Coefficient (Pearson coefficient or Pearson’s r). The coefficient can range from –1 to +1, with negative coefficients (i.e., less than 0) indicative of an inverse relationship and positive coefficients (i.e., greater than 0) indicative of a positive relationship. Common ranges are –.30 to 0 or 0 to .30, described as weak relationship; –.70 to –.30 or .30 to .70, described as moderate; and –1.0 to –.70 or .70 to 1.0, described as strong. Information is also given about the visualization and statistical analysis of relationships using ordinal and nominal data.
Applying AMBIT principles to the training process

Abstract The AMBIT training process involves the application of AMBIT principles, as well as training in them. Learning is central to the AMBIT approach, and the learning principles that are used in the practice of AMBIT are also used in training, starting with close attention to the relationship between trainer and trainee and the requirement to generate epistemic trust for learning to take place. The principles of Active Planning are particularly salient to training, and the elements of sensitive attunement and broadcasting intentions are described in the training context. In addition to the relationship between trainer and trainee, the role of training in strengthening and supporting relationships in both teams and networks is covered.
Testing independence of bivariate censored data using random walk on restricted permutation graph

In this paper, we propose a procedure to test the independence of bivariate censored data, which is generic and applicable to any censoring types in the literature. To test the hypothesis, we consider a rank-based statistic, Kendall’s tau statistic. The censored data defines a restricted permutation space of all possible ranks of the observations. We propose the statistic, the average of Kendall’s tau over the ranks in the restricted permutation space. To evaluate the statistic and its reference distribution, we develop a Markov chain Monte Carlo (MCMC) procedure to obtain uniform samples on the restricted permutation space and numerically approximate the null distribution of the averaged Kendall’s tau. We numerically compare the power of our procedure to existing state of the art procedures in the literature under various censoring types. We apply the procedure to three real data examples with different censoring types, and compare the results with those by existing methods.
Multi-feature combination method for point cloud intensity feature image and UAV optical image matching

LiDAR point clouds and optical images are two widely used geospatial data. The fusion of LiDAR point clouds and optical images can take full advantage of these two types of data. Since LiDAR point clouds and optical images vary in dimension (3D vs. 2D), spectral (near-infrared vs. visible) and data acquisition principles ( time of flight vs. perspective projection), the fusion of LiDAR point clouds and optical images is challenging. This paper deals with the registration of LiDAR point clouds and optical images. Feature point-based matching methods with different feature detector and descriptor combinations are evaluated, and find that different combinations affect the matching performance greatly. Among the evaluated 112 combinations, FAST-SIFT and AGAST-SIFT combinations have the best matching performance. Besides, to remove the large amount mismatches in the matching results, the paper proposed a template and RANSAC based mismatch removal algorithm. The experimental results show that the proposed mismatch removal algorithm greatly improved the matching success rate and the correct matching rate.
Granularity classification method for highways based on granularity synthesis in quotient space

At present, in the related research of 3D line scene, the multi-scale expression method is mostly used to describe the detail degree of geographical features, and there is a lack of a method to describe objects from the perspective of granularity, so it is hard to meet the application requirements of objects in different scenarios. This paper introduces quotient space theory and proposes a representation method of highway geographic entities at different granularity levels. Firstly, the conditions that can be divided into highway geographical entities within the same granularity are determined. Then, the granularity partition model of highway geographic entities is constructed from three aspects: text similarity, literal similarity and semantic distance similarity. Finally, each quotient space is organized by the theory of quotient space particle size synthesis, and the whole geographical entity is divided into multiple granularities, and the experimental verification is carried out. The results show that the method can well meet the multi-level and multi-granularity requirements of geographic entities, and realize the hierarchical optimization of expressway scenes.
A loss-modified encoder-decoder network for image semantic segmentation of remotely sensed data

Environment monitoring, traffic management, and autonomous driving systems are numerous industries that use semantic segmentation of remote observation sensing data. High-resolution remote sensing satellite images have a large application area, include a lot of object information, and are challenging to extract information features due to the rapid growth of remote sensing technology. Motivated by the recent success of Convolutional Neural Network (CNN) for image semantic segmentation, this paper explores the power of CNN feature learning ability for remote sensing images, and serviceable image segmentation results are provided. A conventional Encoder-Decoder segmentation network is proposed for remotely sensed data. To further boost the performance of semantic segmentation, a variety of segmentation losses are explained and utilized. Evaluation metrics are introduced, and a series of experiments are conducted. The results demonstrate our proposed loss-modified CNN model's competitive performance against other methods.
A comparison of algorithms to suppress noise in GF-3 FSII SAR images

Synthetic aperture radar (SAR) image processing using different filters can produce different results for different parts of the image. Thus, specific applications require specific filters. In this study, we evaluated and compared the performance of several widely used filters, including the mean, median, Lee, Frost, Kuan, enhanced Lee, enhanced Frost, and gamma maximum a posteriori (MAP) filters, in suppressing speckle noise in GF-3 FSII images. We applied these filters to different images to determine the optimal filter and processing window size for different image parts, including strongly textured, homogeneous, and fringe areas. We found that the optimal filter for the strongly textured area was the Kuan filter (window size, 7 × 7). The optimal filters for homogeneous areas were the Kuan (7 × 7, 5 × 5, and 3 × 3), Lee (7 × 7), and mean filters (7 × 7). The optimal filters for fringe areas were the enhanced Lee (7 × 7 and 5 × 5) and enhanced Frost filters (7 × 7).
Side-scan sonar image denoising method based on improved NLM algorithm

Side-scan sonar is widely used in maritime search and rescue, submarine geological exploration, target detection and identification, etc. with its long range of action, comprehensive coverage, and large amount of collected data. Image denoising is the premise of the correct interpretation of side-scan sonar images. In this paper, a new weighted kernel function is proposed on the basis of the original Non-Local Means (NLM) denoising algorithm, which leads to an improved non-local mean denoising algorithm. By experimenting with Gaussian white noise images with different noise levels, it appears that the efficiency of this algorithm is superior to that of the NLM algorithm, and the denoising performance is significantly enhanced compared with the NLM and Fast Non-Local Means (FNLM) algorithm, especially for images under solid noise.
Registered Replication Report: Re-examination of the Relationship between Spatial and Empathic Perspective-Taking

Empathic perspective-taking, usually measured with the Interpersonal Reactivity Index, refers to the tendency to adopt the psychological viewpoint of others; whereas spatial perspective-taking, measured with a cognitive task, is the ability to mentally rotate oneself into another’s spatial perspective. Erle and Topolinski (2015, study4) found these two ways of perspective-taking to be correlated, suggesting a shared mechanism between them. However, this finding was not supported by a replication study. We conducted an additional replication study by adding improvements to the replication study (e.g., a-priori power analysis), which again did not find a correlation between the two types of perspective-taking.
Data processing quality control and service application of global ocean stereoscopic observation network

This paper introduces the basic composition of the Global Ocean Stereoscopic Observation Network and expounds the necessity and importance of observation data processing and quality control. Taking data of ocean stations and shore-based radar stations as examples, the paper elaborates the data processing and quality control technology, including processing principles, objects, contents, methods and parameters, and focuses on 16 quality control methods including range test, missing test and statistical characteristic test. The quality control for the sea-level data of the ocean station was carried out, and the error detection rate was 1.8%. The data after quality control was more authentic and reliable. Then, it analyses the quality control software and sharing service platform of the observation data. Lastly, it points out that further studies on homogenization test and correction of ocean station data would make the results more reasonable and scientific.
A High Utility Itemset Mining Algorithm Based on Particle Filter

High utility itemset mining is an interesting research in the field of data mining, which can find more valuable information than frequent itemset mining. Several high-utility itemset mining approaches have already been proposed; however, they have high computational costs and low efficiency. To solve this problem, a high-utility itemset mining algorithm based on the particle filter is proposed. This approach first initializes a population, which consists of particle sets. Then, to update the particle sets and their weights, a novel state transition model is suggested. Finally, the approach alleviates the particle degradation problem by resampling. Substantial experiments on the UCI datasets show that the proposed algorithm outperforms the other previous algorithms in terms of efficiency, the number of high-utility itemsets, and convergence.
FedNSE: Optimal Node Selection for Federated Learning with Non-IID Data

Federated Learning relies heavily on the data available at the worker nodes. In a majority of practical use-cases, the data at the worker nodes are Non-IID (non-independent-and-identically-distributed) in nature. The central server often only selects a subset of the worker nodes to compute the global model. If the subset has more nodes with heterogeneous data, the overall performance would be compromised. Selecting an optimal subset of worker nodes from the available worker nodes is crucial for improving the accuracy and convergence time of Federated Learning models. In this paper, we analyse the impact of Non-IID data, measured using quantity skewness, on the performance of the federated learning model and design approaches to detect the level of skewness of the data distribution at the worker nodes. Following this, we design an approach Federated Node Selection with Entropy (FedNSE) to select the optimal set of worker nodes to improve the accuracy and convergence time of a federated learning model. Our experiments performed in a simulated Federated Learning environment show that using FedNSE that selects the optimal subset of worker nodes improves the convergence time and accuracy compared to the existing approaches with a minimum decrease of 10% in the training loss.
A ZK-SNARK based Proof of Assets Protocol for Bitcoin Exchanges

This paper proposes a protocol for Proof of As-sets of a bitcoin exchange using the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (ZK-SNARK) without revealing either the bitcoin addresses of the exchange or balances associated with those addresses. The proof of assets is a mech-anism to prove the total value of bitcoins the exchange has the authority to spend using its private keys. We construct a privacy-preserving ZK-SNARK proof system to prove the knowledge of the private keys corresponding to the bitcoin assets of an exchange. The ZK-SNARK toolchain helps to convert an NP-Statement for proving the knowledge of the private keys (known to the exchange) into a circuit satisfiability problem. In this protocol, the exchange creates a Pedersen commitment to the value of bitcoins associated with each address without revealing the balance. The simulation results show that the proof generation time, size, and verification time are efficient in practice.
Clarity: Analysing Security in Web Applications

The rapid rise in business' moving online has resulted in e-commerce web applications becoming increasingly targeted by hackers. This paper proposes Clarity, a dynamic black box vulnerability scanner capable of detecting Cross-Site Scripting, SQL Injection, HTTP Response Splitting, and Session Management vulnerabilities in web applications. The developed tool employs the use of Mechanize and Selenium to perform the majority of its web scraping requirements. Clarity was tested against 50 e-commerce web applications, uncovering Session Management flaws as the most prevalent vulnerability, with 36 out of the 50 applications being vulnerable.
Finding Survivors in a Terrestrial Region: A Solution to the Unmanned Aerial Vehicle Trajectory Planning Problem

Unmanned Aerial Vehicles (UAVs) are often employed for a wide range of tasks such as military, reconnais-sance, commercial, agriculture, disaster management, product delivery, research, etc. Among these, drones are crucial during a disaster since they must assess the damage and quickly locate the survivors under the rubble. Drones fly autonomously or operated by remote pilots. In autonomous drones, trajectories are programmed to identify the survivors in a given coverage area. This is mapped to a trajectory planning problem for complete coverage with minimal time and minimal energy. In this paper, we propose a novel two-stage algorithm for finding survivors in a terrestrial region. In the first stage, an efficient waypoint generation algorithm for locating the waypoints is designed and in the second stage, a column-by-column traversal algorithm is designed to effectively traverse the generated waypoints. The experiments are conducted in real time using Tello drones. From the experiments, it is inferred that our waypoint generation algorithm results in a lower number of waypoints than the other existing techniques. Our traversal algorithm is as efficient as the existing spiral algorithm.