Data Processing Model Research Articles

Analysis of executive transaction characteristics based on machine learning cross-validation

Executive trading is a kind of investment strategy that analyses and models the financial market with mathematical methods. Machine learning requires computer programs to improve their performance at specific tasks by learning on data sets. Both require the extraction of information from data, so with the success of machine learning in recent years, there has been a strong interest in the combination of executive trading and machine learning methods in both industry and academia. In this paper, based on machine learning is complete, a more complete model of data processing - build - generated forecast - trading strategy - back-test analysis “research framework, and discusses the structures of branching and merging model structure set up, based on the value relevance of the output value and supervision model selection criteria, independently of the back and trading strategy model assessment method and a series of important issues. This paper focuses on the innovative exploration of the application of the loss function, and puts forward several loss functions based on the portfolio point of view, among which the negative cosine loss function shows significant advantages and strong practicability in stability and gain. Corresponding empirical research also reveals the application value and limitation of other loss functions.

Study of Geostatistical Inversion in the Lithologic Distribution and Velocity Modeling of Thick Igneous Rock in the FY Area, Northern Tarim Basin, China

Abstract —In the northern Tarim Basin, a large number of thick igneous rocks are encountered in the drilling process in the Permian. Their lithology and velocity are very strongly, which has a great influence on migration imaging of the “beaded” areas. It is very important to conduct the fine lithology identification and high-precision velocity modeling of the igneous rocks for the exploration and development of the reservoirs. A geostatistical inversion method to obtain the igneous-rock lithologic distribution pattern and velocity modeling in the FY area of the northern Tarim Basin is introduced in this paper. The results show that the application of the geostatistical inversion method greatly improves the resolution of lithology identification. This helps us further understand the Permian igneous rocks distribution in the FY area. Comparison between the seismic facies classification maps of the FY study area shows that the obtained velocity model can reflect the lateral distribution of igneous rocks well. At the same time, the velocity model can reflect the variation of igneous rocks velocity in detail and has a high precision. The average velocity error of the wells participating in the inversion is less than 2%, and the minimum average velocity error is 0.23%. Finally, the velocity model is applied to seismic data processing, and the processing results indicate that it can help to improve seismic migration imaging. The study demonstrates that the geostatistical inversion method can provide a high-precision velocity model for formation pressure prediction and seismic data processing and interpretation, ultimately guiding the exploration and development of oil.

Recommendations for statistical methods usage for processing data of pedagogical experiments

In some institutions of continuous education in the Republic of Moldova there is no course in Applied Statistics for Teacher taught. Because of this, the graduates encounter some difficulties in processing the experimental data. Through this article, the authors propose suggestions and models for data processing for additional qualification / requalification theses.

Students’ informal statistical inferences through data modeling with a large multivariate dataset

ABSTRACT In today’s age of information, the use of data is very powerful in making informed decisions. Data analytics is a field that is interested in identifying and interpreting trends and patterns within big data to make data-driven decisions. We focus on informal statistical inference and data modeling as a means of developing students’ data analytics skills in school. In this study, we examine how students apply the data modeling process to draw informal inferences when exploring trends, patterns and relationships in a real dataset using technological tools, such as CODAP and Excel. We analyzed 17–18-year-old students’ written reports on their explorations of data supplied by third parties. Students used a variety of statistical measures and visualizations to account for variability in analyzing data. They tended to make statements with certainty in their inferences and predictions beyond the data. When the pattern in the data was uncertain, they were inclined to use contextual knowledge to remain certain in their claims.

Defending CNN against privacy leakage in edge computing via binary neural networks

As the IoT has developed, edge computing has played an increasingly important role in the IoT ecosystem. The edge computing paradigm offers low latency and high computing performance, which is conducive to machine learning tasks such as object detection in autonomous driving. However, data privacy risks in edge computing still exist and the existing privacy-preserving methods are not satisfactory due to the large computational overhead and unbearable accuracy loss.We have designed a privacy-preserving machine learning framework for both user and cloud data. Users and the cloud provide data for inference and training respectively, and the privacy protection of these two aspects is both considered in this paper. Users provide test data and want to access the data-processing models in cloud for inference, and the cloud provides the training data used for training an eligible model. For user data, in order to maintain the overall performance of the machine learning framework while using homomorphic encryption, instead of providing encrypted data to all machine learning tasks, we divide the neural network into two parts, with one part kept on the trusted edge and provided with plaintext, and the other deployed on the untrusted cloud and provided with encrypted input. For cloud data, we apply the binary neural network, a network with the binarized value of weights. This method is practical for narrowing the confidence score gap (between the training and test sets) predicted by the model, which accounts most for a successful exploratory attack on training data. Experiments demonstrate that the results of the adversary’s membership inference attack are close to random guessing, and the accuracy is only slightly affected. Compared with the unencrypted network on VGG19, when the network is split from conv4_1 to fc8, the efficiency of using HE is only 100 to 30 times slower.

Gravity Field Recovery and Error Analysis for the MOCASS Mission Proposal Based on Cold Atom Interferometry

Satellite missions providing data for a continuous monitoring of the Earth gravity field and its changes are fundamental to study climate changes, hydrology, sea level changes, and solid Earth phenomena. GRACE-FO (Gravity Recovery and Climate Experiment Follow-On) mission was launched in 2018 and NGGM (Next Generation Gravity Mission) studies are ongoing for the long-term monitoring of the time-variable gravity field. In recent years, an innovative mission concept for gravity measurements has also emerged, exploiting a spaceborne gravity gradio-meter based on cold atom interferometers. In particular, a team of researchers from Italian universities and research institutions has proposed a mission concept called MOCASS (Mass Observation with Cold Atom Sensors in Space) and conducted the study to investigate the performance of a cold atom gradiometer on board a low Earth orbiter and its impact on the modeling of different geophysical phenomena. This paper presents the analysis of the gravity gradient data attainable by such a mission. Firstly, the mathematical model for the MOCASS data processing will be described. Then numerical simulations will be presented, considering different satellite orbital altitudes, pointing modes and instrument configurations (single-arm and double-arm); overall, data were simulated for twenty different observation scenarios. Finally, the simulation results will be illustrated, showing the applicability of the proposed concept and the improvement in modeling the static gravity field with respect to GOCE (Gravity Field and Steady-State Ocean Circulation Explorer).

Performance of soft computing techniques for GNSS data processing and point displacement modeling for suspension bridge

Performance of soft computing techniques for GNSS data processing and point displacement modeling for suspension bridge

Spatiotemporal distribution analysis of Vespa Mandarinia based on GM model

As a natural enemy of the European honeybee, the Chinese great steller’s wasp can have a serious impact on local bee populations, so they are considered an agricultural pest. After the emergence of these pests, countries must decide how to prioritize their limited resources for follow-up investigations. In this paper, a model was established to predict the emergence of wild silkworm, which could be used to provide strong support for pest management. when predicting how pests spread over time, we built a GM model to predict the spread of Vespa mandarinia from the spatial and temporal distribution. When predicting the geographic location of Vespa mandarinia, we set up a two-dimensional GM model using longitude and latitude as two variables to predict the longitude and latitude of the next Vespa mandarinia. At the same time, when predicting the occurrence time of Vespa mandarinia, we formed a sequence of the annual 12-month report times of previous years, and still used GM model for data processing and prediction.

Machine learning in the development of regulators: the specifics of data preparation and the choice of the ML model algorithm

The article considers the stages of regulators development based on machine learning. Machine learning, like a fuzzy logic, is used in PID regulators in two ways: it is able to build the regulator or a unit for adjusting its coefficients. A feature of machine algorithms is the “learning” ability which allows you to transfer the experience of an expert to the system. The presence of a learning procedure distinguishes the configuration of regulators based on machine algorithms from the classical approach. The publication analyzed the possibility of using regression machine algorithms (models) of the Scikit-learn library (Python) to solve control problems using the example of a level regulator. The search for an effective data processing model led to the construction of ensemble algorithms based on stacking. The high degree of compliance between predicted and test data confirms the accuracy and stability of the results. The results presented in the article can be used in a simulating or in developing a controller based on a machine algorithm.

Feasibility Queries in Distributed Architectures - Concept and Implementation in HiGHmed.

Medical routine data promises to add value for research. However, the transfer of this data into a research context is difficult. Therefore, Medical Data Integration Centers are being set up to merge data from primary information systems in a central repository. But, data from one organization is rarely sufficient to answer a research question. The data must be merged beyond institutional boundaries. In order to use this data in a specific research project, a researcher must have the possibility to query available cohort sizes across institutions. A possible solution for this requirement is presented in this paper, using a process for fully automated and distributed feasibility queries (i.e. cohort size estimations). This process is executed according to the open standard BPMN 2.0, the underlying process data model is based on HL7 FHIR R4 resources. The proposed solution is currently being deployed at eight university hospitals and one trusted third party across Germany.

Big Data Integration and Processing Model

The presented paper deals with data integration and sorting of Covid-19 data. The data file contains fifteen data fiels and for the design of integration and sorting model each of them is configured in data type, format and field length. For the data integration and sorting model design Talend Open Studio is used. The model concerns the performance of four main tasks: data integration, data sorting, result display, and output in .xls file format. For the sorting process two rules are assigned in accordance with the medical and biomedical requirements, namely to sort report date descending order and the Country Name field in alphabetical one

A data mining based approach for process identification using historical data

ABSTRACT In this paper, a data mining based methodology for process identification from historical data was proposed. Thereon, it considers the phases of process understanding, data collection, data preparation, data modeling, and model evaluation. As some parts of historical data are irrelevant, a data selection step, based on the Gaussian Mixture Model (GMM) clustering algorithm, was considered. Additionally, the methodology includes a data informativity step to study the richness of data. In this regard, the condition number (CN) and the extended CN for ridge regression (RR CN) were used. To evaluate the approach, 2 years of industrial thickener historical data were used. Thereafter, data were prepared and an ARX (Auto-Regressive with eXogenous inputs) model structure was adopted to identify the model. To estimate input delays, Granger causality was used. As for fit criteria, least square regression was tested and compared to ridge regression as a less sensitive method to multicollinearity. The results were then evaluated based on the 20-step ahead prediction and compared to existing methods. In this context, the proposed approach gave the best results with an R 2 of 98.11% and 62.70% for 1 and 20-step ahead predictions, respectively.

A Many-Objective Optimization Based Intelligent High Performance Data Processing Model for Cyber-Physical-Social Systems

As an emerging paradigm, Cyber-Physical Social System (CPSS) can provide humans with efficient, convenient and personalized services. However, due to the explosive growth of user data, massive and high-dimensional data has become a key issue for CPSS services. In the past, researchers mainly focused on big data analysis methods, ignoring the processing of high-dimensional data before analysis. In response to above problem, this paper focus on the redundant attributes in high-dimensional data. By using intelligent optimization technology, the vision model is optimized for four conflicting goals at the same time, so as to better solve the problem. In order to support this model, a collaboration mechanism (MaOEA-CM) was designed. The algorithm uses the combination of sum of objectives and shift-based density estimation ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${I}_{SDE} {+}$</tex-math></inline-formula> ) indicator as the criterion of gradual co-evolution, and guides individuals to search in the high convergence and multiple directions of CPSS. Experiments on four data sets show that the proposed algorithm and model can significantly improve the accuracy of data processing in CPSS and have higher performance than other feature selection models.

INFLUENCES OF SERVICES QUALITY TOWARDS ON PORT-SERVICE USERS SATISFACTION AT PT PELABUHAN INDONESIA IV (PERSERO) CABANG MAKASSAR

State Owned Enterprise has the main task of providing economic services and goods that the private sector does not provide, as well as providing services related to all the needs and needs of the community. This study aims to explore further the effect of the quality of service provided by PT Pelabuhan Indonesia IV (Persero) Branch Makassar on the satisfaction felt by consumers. The sample in this study was obtained through non-probability sampling techniques and purposive sampling to take samples from research respondents. A total of thirty respondents were involved as samples in this study. The data processing model that will be carried out to analyze the results of this study is a simple regression analysis which is then processed by the SPSS application. Based on the results of the analysis of the coefficient of determination obtained from this study, the variable service quality has a 70.8% influence on the customer satisfaction variable. The results also show that customer satisfaction is positively and significantly affected (0.000 &lt;0.05) by the quality of service provided by the company.

An Approach to Incorporate Subsampling Into a Generic Bayesian Hierarchical Model

The goal of this article is to provide a way for Bayesian statisticians to incorporate subsampling directly into the Bayesian hierarchical model of their choosing without imposing additional restrictive model assumptions. We are motivated by the fact that the rise of “big data” has created difficulties for statisticians to directly apply their methods to big datasets. We introduce a “data subset model” to the popular “data model, process model, and parameter model” framework used to summarize Bayesian hierarchical models. The hyperparameters of the data subset model are specified constructively in that they are chosen such that the implied size of the subset satisfies predefined computational constraints. Thus, these hyperparameters effectively calibrate the statistical model to the computer itself to obtain predictions/estimations in a prespecified amount of time. Several properties of the data subset model are provided including: propriety, partial sufficiency, and semi-parametric properties. Simulated datasets will be used to assess the consequences of subsampling, and results will be presented across different computers to show the effect of the computer on the statistical analysis. Additionally, we provide a joint analysis of a high-dimensional dataset (roughly 10 gigabytes) consisting of 2018 5-year period estimates from the U.S. Census Bureau’s Public Use Micro-Sample (PUMS).

Ntelligent Estimation of Social Media Sentimental Features using Deep Learning with Natural Language Processing Strategies

In this digital world, experience sharing, knowledge exploration, taught posting and other related social exploitations are common to every individual as well as social media/network such as Face Book, Twitter, etc plays a vital role in such kinds of activities. In general, many social network based sentimental feature extraction details and logics are available as well as many researchers work on that domain for last few years. But all those research specification are narrowed in the sense of building a way for estimating the opinions and sentiments with respect to the tweets and posts the user raised on the social network or any other related web interfacing medium. Many social network schemes provides an ability to the users to push the voice tweets and voice messages, so that the voice messages may contain some harmful as well as normal and important contents. In this paper, a new methodology is designed called Intensive Deep Learning based Voice Estimation Principle (IDLVEP), in which it is used to identify the voice message content and extract the features based on the Natural Language Processing (NLP) logic. The association of such Deep Learning and Natural Language Processing provides an efficient approach to build the powerful data processing model to identify the sentimental features from the social networking medium. This hybrid logic provides support for both text based and voice based tweet sentimental feature estimations. The Natural Language Processing principles assists the proposed approach of IDLVEP to extracts the voice content from the input message and provides a raw text content, based on that the deep learning principles classify the messages with respect to the estimation of harmful or normal tweets. The tweets raised by the user are initially sub-divided into two categories such as voice tweets and text tweets. The voice tweets will be taken care by the NLP principles and the text enabled tweets will be handled by means of deep learning principles, in which the voice tweets are also extracted and taken care by the deep learning principle only. The social network has two different faces such as provides support to developments as well as the same it provides a way to access that for harmful things. So, that this approach of IDLVEP identifies the harmful contents from the user tweets and remove that in an intelligent manner by using the proposed approach classification strategies. This paper concentrates on identifying the sentimental features from the user tweets and provides the harm free social network environment to the society.

Energy efficiency prediction model and energy characteristics of subsea disc pump based on velocity slip and similarity theory

The variable-speed energy efficiency prediction model (EEPM) of subsea disc pump is the key to achieve double gradient in deepwater drilling, and it has also been the difficulty of pressure control matching and energy conservation. In this paper, the radial slip vortices are fully considered, and average slip method of circumferential velocity, relative velocity and absolute velocity at impeller outlet is proposed. Based on hydraulic energy loss and mechanical energy loss, the calculation method of theoretical head and similar parabola in similarity theory is improved, and EEPM is established. In addition, a new multifunctional test system and data processing model are designed to reduce the error. The experimental results show that the accuracy of EEPM is increased by about 4% when deviating from the rated speed by more than 20%, and the accuracy is about 99% within 20%. From the simulation, the flow passage is mainly a mixed-type movement, such as S-type double vortex and O-type single vortex. The turbulence kinetic energy near the volute tongue is the largest. The study improves the energy efficiency of drilling pressure regulation, and also provides a new perspective to the energy efficiency optimization and energy conservation of similar centrifugal pump and vortex pump.

Exploring mental models of the right to informational self-determination of office workers in Germany

AbstractApplied privacy research has so far focused mainly on consumer relations in private life. Privacy in the context of employment relationships is less well studied, although it is subject to the same legal privacy framework in Europe. The European General Data Protection Regulation (GDPR) has strengthened employees’ right to privacy by obliging that employers provide transparency and intervention mechanisms. For such mechanisms to be effective, employees must have a sound understanding of their functions and value. We explored possible boundaries by conducting a semi-structured interview study with 27 office workers in Germany and elicited mental models of the right to informational self-determination, which is the European proxy for the right to privacy. We provide insights into (1) perceptions of different categories of data, (2) familiarity with the legal framework regarding expectations for privacy controls, and (3) awareness of data processing, data flow, safeguards, and threat models. We found that legal terms often used in privacy policies used to describe categories of data are misleading. We further identified three groups of mental models that differ in their privacy control requirements and willingness to accept restrictions on their privacy rights. We also found ignorance about actual data flow, processing, and safeguard implementation. Participants’ mindsets were shaped by their faith in organizational and technical measures to protect privacy. Employers and developers may benefit from our contributions by understanding the types of privacy controls desired by office workers and the challenges to be considered when conceptualizing and designing usable privacy protections in the workplace.

A Study on the Qualitative Data Analysis for Customer Experience Management Strategy

Securing customer experience data that creates positive emotions for customers and differentiates them from products and services from competitors is becoming important to a company's growth engine. In particular, an important factor in the management of experience data requires a qualitative-based experience data processing method to secure good experience data different from the quantitative data collection such as big data and processing method. With the emergence of the experience economy, it is very important for companies to collect and process experience data in the existing big data processing method. However, the experience data processing method based on big data that analyses the current quantitative data is difficult to provide good experience data from a corporate data strategic point of view. In particular, for corporate customer experience management, mix studies are required for analysis method of qualitative experience data to meaningfully interpret the expansive quantitative experience data of big data and phenomena and context in social science. This is because it is possible to discover the meaning of experience data by reading the context of phenomena by collecting experiences through ethnography methods such as observation or interviewing the context that could not be read in the process of processing the vast quantitative experience data of the big data method. In this study, the first processing was performed as an affinity diagram through a method of collecting experience data using ethnography method. Secondly, the effect of the qualitative experience data processing method on customer experience management, customer loyalty reinforcement, and enterprise value creation was studied. As a result, only the research hypothesis that there was a direct relationship between the affinity method and the utilization of experience data was rejected, and all the research settings set for the remaining qualitative experience data processing and utilization model were adopted.

MTStemmer: A multilevel stemmer for effective word pre-processing in Marathi

In natural language processing, it is important that the context and the meaning of words are retained while also ensuring the efficacy of the data modelling process. During human-to-human interactions, special care is taken regarding the tense and phrasing of the words by taking into consideration the rules of grammar of the specific language. While this modification of words is necessary for framing consistent sentences, these appendages do not add significant value to the original meaning of the word. Stemming is the process of converting words back to their root form for efficient and accurate modelling of the data. In this paper, MTStemmer, a new stemmer for the Marathi language is proposed. It focuses on the stripping of suffixes for obtaining the root word form. The proposed stemmer applies a multilevel approach by taking into consideration both auxiliary verb-based suffixes and gender-based suffixes. The presented approach intends to improve upon the limitations of the previously proposed stemmers for this language. The stemming performed by the stemmer is found to be more accurate in terms of mapping to the root words. Stemming is often an important pre-processing step before processing the data further for the main task. The benefit of the proposed stemmer is demonstrated by using it for an extractive Marathi text summarization task. A significant improvement in the performance of multiple performance metrics is achieved owing to the stemming done by MTStemmer. The working of the proposed stemmer shows promising signs for the development of similar engines for other Indic languages.