Reviewerly/exHarmony huggingface dataset:
Research on Privacy Protection Methods for Data Mining

With the applications of big data and cloud computing technologies in industries, data mining technologies have been developing rapidly in these years. However, privacy issues have been attracting attentions for users and researchers since the laws and regulations of protecting personal information are issued. How to appropriately apply data mining technologies while meeting the privacy protection requirements become an important problem to address. In this paper, the privacy preserving data mining technologies are studied including K-means, Support Vector Machine, decision tree and association rule mining. In addition to their principles, the corresponding privacy protection methods for them are discussed. Furthermore, the commonly used privacy protection methods are studied including restricted release, searchable symmetric encryption, homomorphic encryption and digital envelope. Finally, the suggestions are given that the data processing algorithms need to be improved to obtain the better balance between data mining efficiency and privacy protection, and the system could be designed to provide privacy protection measures to meet personalized demands. The studies in this paper are expected to provide technical ideas to various service providers such as personal recommendation to implement privacy protection strategies.
“I Want to …Build”: Research on Dam Transshipment Facilities of Typical Hydropower Stations on the Right Bank of Jinsha River

At present, with the increase of waterway cargo transportation volume, the advantages of waterway transportation cannot be brought into full play because some hydropower stations are constructed without considering the construction of navigation facilities and dam transshipment facilities, or without considering the currency capacity of built navigation buildings. In view of this, this paper takes the typical hydropower station on the right bank of Jinsha River as the research object. According to the hydrological conditions of the reservoir area and the topographical and geological conditions of the upstream and downstream of the power station, the location of the dam transshipment wharf and the dam transshipment highway transportation mode of the typical hydropower stations are summarized by combining quantitative and qualitative analysis. Taking coal transportation as an example, we have studied the transportation modes of water-highway-water transport over dam from Huize to Shuifu Port for three times, from Qiaojia to Shuifu Port transport over dam for two times, and from Yongshan to Shuifu Port transport over dam for one time by using the economic comparative analysis method. The results show that the new mode of bulk cargo transportation proposed in this paper can meet the high-quality development needs of Jinsha River navigation in terms of safety and efficiency; based on the high mountains and valleys and complex geological conditions, the “Z” shape general layout scheme of the grading platform is innovatively proposed; water-highway-water transport over dam has certain advantages in transportation cost, and the transportation cost is the lowest when the dam is overturned twice; during the water-highway-water transport over dam, the highway transportation distance should be shortened as far as possible to improve the economic benefits of dam turnover transportation.
Research on Data Security Storage System Based on Distributed Database

Data security storage technology is an important part of information management. The security of data storage is the key to user information security, system operation efficiency and economic benefits. The implementation of data security storage technology plays an important role in improving the efficiency of data management and reducing the operating costs of enterprises. Due to the data leakage caused by intentional or unintentional wrong operation, which infringes the user’s personal privacy, the traditional data storage methods face greater risks in the operation process. These disadvantages affect the use effect of the system, and even lead to system paralysis and data loss in serious cases. Distributed database has the characteristics of high degree of structure, good openness and small storage space requirements, and plays an important role in data management. In this paper, a data security storage model is studied combining the symmetric encryption algorithm, asymmetric encryption algorithm and the hash algorithm. A data security storage system is then proposed based on distributed storage while its functional composition, data transmission framework and database design are described. The proposed system in this paper is expected improve the data storage security so as to reduce the risks of information leakage.
Quantum Information Processing via Indirect Quantum Control

Quantum information processing will enable us to solve problems that conventional computers cannot or provide informationally secure cryptography.However, there are still difficulties in achieving quantum information processing.This article introduces indirect quantum control as one method to overcome these difficulties and shows that indirect quantum control has excellent properties for achieving quantum information processing.
The Covid-19 app that lasted two weeks: how technology may embody controversial public policies for health care and why we should worry about it

Abstract This paper describes the case of a Brazilian government app (TrateCov) that was deployed for only 2 weeks during the emergence of the SARS-CoV-2 Gamma variant. TrateCov was intended as a decision support tool to assist health care professionals with the diagnosis and treatment of COVID-19. After the tool was inadvertently released on a public Web site, it became accessible to anyone with an Internet connection, raising the risk of inappropriate self-management. Moreover, experts and journalists quickly spotted that almost all symptoms entered into the app resulted in a diagnosis of COVID-19, along with a recommendation to use the same ‘Covid kit’ of unproven drugs favored by leading politicians, and in the same doses for adults, children, and pregnant women. The institutional and social backlash was immediate, forcing the Brazilian Ministry of Health to take TrateCov offline. The case illustrates the potential for weak evidence and ideological bias to infiltrate digital health technologies, creating risks for patient safety and diversion of resources, thus emphasizing the importance of good governance at all stages of the innovation lifecycle. The speed of public health crises presents particular challenges for managing this phenomenon, calling for anticipatory steps to avoid such risk as part of responsible pandemic prevention strategies
The Sequence Recommendation Algorithm based on GRU and Attention

In recent years, with the booming development of social networks and e-commerce, users have increasingly convenient access to information, while a large amount of data continues to emerge, leading to more and more serious information overload. To alleviate the problem of information overload, recommendation systems have emerged to assist users in sifting through massive amounts of information to find content that meets their needs. Sequential recommendation, as a form of recommendation system, mainly analyzes the interaction behavior between users and items, models user characteristics, and then uses various methods to capture users' long-term and short-term preferences to recommend items of interest to users. Based on the perspective of user preference change over time, this paper provides an in-depth analysis of the current research progress and methods of user behavior sequence recommendation. At the same time, this paper proposes corresponding solution strategies for the problems of cold start, sparse matrix and noise interference faced by traditional recommendation systems. Finally, we will discuss the challenges and future research directions of recommendation systems to provide the theoretical basis for further improvement of recommendation systems.
The Effect of Motivation to Participate in the World Knowledge Forum on Overall Satisfaction: Focused on Mediate Effect of Perceived Value

It is confirmed that there is a limit to the lack of convention that are maintained for a long time with continuity in Korea. Therefore, the purpose of this study is to identify the motivation for participating in the World Knowledge Forum, which has been held continuously for 23 years, and to investigate how it affects the value and satisfaction perceived by the participants. Through this, it is intended to identify what kind of efforts should be made to ensure the continuity of convention, focusing on participation motivation, perceived value, and overall satisfaction from the participant"s point of view. Participants" motives for participation in the World Knowledge Forum were categorized into four factors: educational, friendly, deviant, and playful. The mediating effect of perceived value in the relationship of overall satisfaction is verified.
DiffPrep: Differentiable Data Preprocessing Pipeline Search for Learning over Tabular Data

Data preprocessing is a crucial step in the machine learning process that transforms raw data into a more usable format for downstream ML models. However, it can be costly and time-consuming, often requiring the expertise of domain experts. Existing automated machine learning (AutoML) frameworks claim to automate data preprocessing. However, they often use a restricted search space of data preprocessing pipelines which limits the potential performance gains, and they are often too slow as they require training the ML model multiple times. In this paper, we propose DiffPrep, a method that can automatically and efficiently search for a data preprocessing pipeline for a given tabular dataset and a differentiable ML model such that the performance of the ML model is maximized. We formalize the problem of data preprocessing pipeline search as a bi-level optimization problem. To solve this problem efficiently, we transform and relax the discrete, non-differential search space into a continuous and differentiable one, which allows us to perform the pipeline search using gradient descent with training the ML model only once. Our experiments show that DiffPrep achieves the best test accuracy on 15 out of the 18 real-world datasets evaluated and improves the model's test accuracy by up to 6.6 percentage points.
Efficient Personalized PageRank Computation: The Power of Variance-Reduced Monte Carlo Approaches

Personalized PageRank (PPR) computation is a fundamental problem in graph analysis. The state-of-the-art algorithms for PPR computation are based on a bidirectional framework which include a deterministic forward push and a Monte Carlo sampling procedure. The Monte Carlo sampling procedure, however, often has a relatively-large variance, thus reducing the performance of the PPR computation algorithms. To overcome this issue, we develop two novel variance-reduced Monte Carlo techniques for PPR computation. Our first technique is to apply power iterations to reduce the variance of the Monte Carlo sampling procedure. We prove that conducting few power iterations can significantly reduce the variance of existing Monte Carlo estimators, only with few additional costs. Moreover, we show that such a simple and novel variance-reduced Monte Carlo technique can achieve comparable estimation accuracy and the same time complexity as the state-of-the-art bidirectional algorithms. Our second technique is a novel progressive sampling method which uses the historical information of former samples to reduce the variance of the Monte Carlo estimator. We develop several novel PPR computation algorithms by integrating both of these variance reduction techniques with two existing Monte Carlo sampling approaches, including random walk sampling and spanning forests sampling. Finally, we conduct extensive experiments on 5 real-life large graphs to evaluate our solutions. The results show that our algorithms can achieve much higher PPR estimation accuracy by using much less time, compared to the state-of-the-art bidirectional algorithms.
IMPACTOS DA PANDEMIA DA COVID-19 NO TRABAHO DE ASSISTENTES SOCIAIS: PRECARIZAÇÃO E ADOECIMENTO PROFISSIONAL

The purpose of this article is to analyze the overexploration of female labor in the capitalist, system originated in patriarchy, in which the women becomes another instrument for the production of surplus value.The goal is to present the exploration and oppression that women have been lived in the social and sexual division of labor, whose situation has been gained visibility from the organization of the feminist movement.
Compound feature attention network with edge enhancement for low-dose CT denoising

BACKGROUND: Low-dose CT (LDCT) images usually contain serious noise and artifacts, which weaken the readability of the image. OBJECTIVE: To solve this problem, we propose a compound feature attention network with edge enhancement for LDCT denoising (CFAN-Net), which consists of an edge-enhanced module and a proposed compound feature attention block (CFAB). METHODS: The edge enhancement module extracts edge details with the trainable Sobel convolution. CFAB consists of an interactive feature learning module (IFLM), a multi-scale feature fusion module (MFFM), and a joint attention module (JAB), which removes noise from LDCT images in a coarse-to-fine manner. First, in IFLM, the noise is initially removed by cross-latitude interactive judgment learning. Second, in MFFM, multi-scale and pixel attention are integrated to explore fine noise removal. Finally, in JAB, we focus on key information, extract useful features, and improve the efficiency of network learning. To construct a high-quality image, we repeat the above operation by cascading CFAB. RESULTS: By applying CFAN-Net to process the 2016 NIH AAPM-Mayo LDCT challenge test dataset, experiments show that the peak signal-to-noise ratio value is 33.9692 and the structural similarity value is 0.9198. CONCLUSIONS: Compared with several existing LDCT denoising algorithms, CFAN-Net effectively preserves the texture of CT images while removing noise and artifacts.
A Combined Usage of NLP Libraries Towards Analyzing Software Documents

Software documents are commonly processed by natural language processing (NLP) libraries to extract information. The libraries provide similar functional APIs to achieve NLP tasks, numerous toolkits result in a problem of selection. In this work, we propose a method to combine the strengths of different NLP libraries to avoid the subjective selection of a specific NLP library. The combined usage is conducted through two steps, i.e. document-level selection of primary NLP library and sentence-level overwriting. The primary NLP library is determined according to the overlap degree of the results. The highest overlap degree indicated the most effective NLP library on a specific NLP task. Through sentence-level overwriting, the possible fine-gained improvements from other libraries are extracted to overwrite the outputs of primary library. We evaluate the combined method with six widely used NLP libraries and 200 documents from three different sources. The results show that the combined method can generally outperform all the studied NLP libraries in terms of accuracy. The finding means that our combined method can be used instead of individual NLP library for more effective results.
Soft Computing Techniques for Detecting Cyberbullying in Social Multimedia Data

Cyberbullying is a form of abuse, manipulation, or humiliation directed against a single person via the Internet. CB makes use of nasty Internet comments and remarks. It occurs when someone publicly mocks, insults, slanders, criticizes, or mocks another person while remaining anonymous on the Internet. As a result, there is a rising need to create new methods for sifting through data on social media sites for symptoms of cyberbullying. The goal is to lessen the negative consequences of this condition. This article discusses a soft computing-based methodology for detecting cyberbullying in social multimedia data. This model incorporates social media data. Normalization is performed to remove noise from data. To improve a feature, the Particle Swarm Optimization Technique is applied. Feature optimization helps to make cyberbullying detection more accurate. The LSTM model is used to classify things. With the help of social media data, the PSO LSTM model is getting better at finding cyberbullying. The accuracy of PSO LSTM is 99.1%. It is 2.9% higher than the accuracy of the AdaBoost technique and 10.4% more than the accuracy of the KNN technique. The specificity and sensitivity of PSO-based LSTM is also higher in percentage than KNN and AdaBoost algorithm.
SMART WASTE MANAGEMENT SYSTEM BASED ON THE INTERNET OF THINGS

The time has come to segregate waste at the household level because doing so at the dump is cumbersome and frequently ineffective.With the least amount of work possible, this study seeks to ensure proper waste sorting while minimizing human involvement in the process.The automatic garbage segregator system suggested in this paper is based on IOT.It helps with real-time monitoring of rubbish levels in dustbins and detects and separates waste at the household level into dry, wet, and metallic waste.The device uses an ultrasonic sensor to detect the arrival of rubbish, and then a metallic sensor to determine whether there is any metal present in the waste.Additionally, the waste is divided between dry and moist residue using the capacitive detector.The wipers assist in moving the waste over the appropriate bins once it has been identified and sorted, after which the platform turns.Additionally, the system uses an ultrasonic sensor to display the amount of trash in the trash cans and an Arduino to provide a location through GPS Tracker so that it can be cleaned if it is full.According to experimental findings, the proposed system has been successful in separating garbage into wet and dry waste and metallic and non-metallic waste.
Interferometric measurement of the radius of curvature based on axial displacement from a confocal position and corresponding defocus wavefront

The radius of curvature (R) is a fundamental parameter of spherical optical surfaces. The measurement range of the widely adopted traditional interferometric method is limited by the length of the precision linear guide rail carrying the measured surface from the cat's eye to the confocal position, and the test result is vulnerable to airflow and vibration in the test environment. An interferometric method is proposed for the radius measurement of spherical surfaces based on a small axial moving distance and the corresponding defocus wavefront to eliminate the dependence on a long guide rail and extend the measuring range. To eliminate the influence of the test environment and calculate the R, a defocus transform algorithm is proposed to instantaneously measure the defocus wavefront from a single interferogram. Numerical simulations theoretically demonstrate that there is no limit to the measurement range of this method because only a short distance of the measured mirror must be moved. A spherical mirror with a radius of curvature of 101.6087 mm is experimentally tested, and the relative measurement error is 0.037%. This method can achieve high accuracy for optical shops and greatly increase the measurement range of the interferometric method without additional equipment.
Transformer-Based Conditional Language Models to Generate Filipino News Articles

The study aims to develop an application that, conditioned on an image, generates a Filipino news article that is similar to a real article.It explores the capabilities of current NLP algorithms for language generation in the low-resource Filipino language.In order to carry out the study, a dataset of Filipino news articles with accompanying images will be collected.With this, the study also sets out to publish a dataset for conditional language generation in the Filipino language.
Apply the Tiling Technique of Heterogeneous Computing on Meta-heuristics

With its multicore technology designed for intensive computation, graphics processing units (GPUs) have advanced tremendously in recent years.The GPU is a multicore coprocessor with dozens or even hundreds of cores designed for intensive computations, unlike a central processing unit (CPU), which has a limited number of cores available.However, CPUs have large caches for accommodating large amounts of data, whereas GPUs have smaller caches but use a massive amount of threads.In order to achieve the best performance from both CPU and GPU, GPU-CPU heterogeneous computing architectures have emerged, resulting in a paradigm shift in parallel computing.Data parallelism, also known as SIMD (single instruction, multiple data), is realized through this paradigm, which allows multiple threads to execute the same task simultaneously on different subsets of the same data.When data parallelism is used to program, it is particularly relevant when there is a lot of data to process, as is often the case in scientific computing.Due to their inherent nature as intensive computing, data parallelism is ideally suited for meta-heuristics.As part of this research, data parallelism is introduced to meta-heuristics, as well as a particular optimization technique called "tiling" in parallel computing."Tiling" refers to grouping threads into tiles to share access to programmable caches on GPUs in order to improve computation speed, particularly for programs that use the same data repeatedly.We introduce the "tiling" technique to ant colony optimization, a meta-heuristic for solving quadratic assignment problem (QAP).A heterogeneous computing architecture is expected to provide benefits in speed, quality, and efficiency over a CPU alone.
Image Augmentation Based Momentum Memory Intrinsic Reward for Sparse Reward Visual Scenes

Many real-life tasks can be abstracted as sparse reward visual scenes, which can make it difficult for an agent to accomplish tasks accepting only images and sparse reward. To address this problem, we split it into two parts: visual representation and sparse reward, and propose our novel framework, called Image Augmentation based Momentum Memory Intrinsic Reward ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">IAMMIR</b> ), which combines self-supervised representation learning with intrinsic motivation. For visual representation, we acquire a representation driven by a combination of image-augmented forward dynamics and reward. To handle sparse reward, we design a new type of intrinsic reward called Momentum Memory Intrinsic Reward ( <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MMIR</b> ), which uses the difference between the outputs from the current model (online network) and the historical model (target network) to indicate the agent's state familiarity. We evaluate our method on a visual navigation task with sparse reward in Vizdoom and demonstrate that it achieves state-of-the-art performance in terms of sample efficiency. Our method is at least 2 times faster than existing methods and reaches a 100% success rate.