Basic principles of TikTok algorithms

2021 ◽  
Vol 5 (2(15)) ◽  
pp. 61-76
Author(s):  
Vasilii Konstantinovich Alekhin ◽  
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Learning Algorithms ◽  
User Interaction ◽  
Machine Learning Algorithms ◽  
Behavioral Patterns ◽  
Basic Principles ◽  
The Difference ◽  
Audio Track ◽  
Database Clustering

Social network TikTok has strong competitive differentiator in comparing with other platforms. ByteDance exploits machine learning algorithms to generate a recommendation feed (for you page). The algorithm bases on two main mechanisms. The first mechanism provides content database clustering depending on the type, audio track, video captions, and hashtags. The second mechanism analyzes the user’s behavioral patterns based on their actions in the application. The next step is the formation of user interaction scenarios. The difference between the predicted behavior and the real one is the object of analysis. If it equals zero, then the recommendations feed is formed correctly. The user is watching more and more interesting videos, just scrolling through video after video.

scholarly journals Machine Learning Model of Dimensionless Numbers to Predict Flow Patterns and Droplet Characteristics for Two-Phase Digital Flows

2021 ◽  
Vol 11 (9) ◽  
pp. 4251
Author(s):  
Jinsong Zhang ◽  
Shuai Zhang ◽  
Jianhua Zhang ◽  
Zhiliang Wang
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Digital Microfluidics ◽  
Flow Patterns ◽  
Machine Learning Algorithms ◽  
Dimensionless Numbers ◽  
Two Phase ◽  
The Difference ◽  
Input Variables ◽  
Digital Microfluidic

In the digital microfluidic experiments, the droplet characteristics and flow patterns are generally identified and predicted by the empirical methods, which are difficult to process a large amount of data mining. In addition, due to the existence of inevitable human invention, the inconsistent judgment standards make the comparison between different experiments cumbersome and almost impossible. In this paper, we tried to use machine learning to build algorithms that could automatically identify, judge, and predict flow patterns and droplet characteristics, so that the empirical judgment was transferred to be an intelligent process. The difference on the usual machine learning algorithms, a generalized variable system was introduced to describe the different geometry configurations of the digital microfluidics. Specifically, Buckingham’s theorem had been adopted to obtain multiple groups of dimensionless numbers as the input variables of machine learning algorithms. Through the verification of the algorithms, the SVM and BPNN algorithms had classified and predicted the different flow patterns and droplet characteristics (the length and frequency) successfully. By comparing with the primitive parameters system, the dimensionless numbers system was superior in the predictive capability. The traditional dimensionless numbers selected for the machine learning algorithms should have physical meanings strongly rather than mathematical meanings. The machine learning algorithms applying the dimensionless numbers had declined the dimensionality of the system and the amount of computation and not lose the information of primitive parameters.

scholarly journals Improving Automatic Control of an Ankle-Foot Prosthesis Using Machine Learning Algorithms

2017 ◽  
Author(s):  
Stuart R. Fairhurst ◽  
Sara R. Koehler-McNicholas ◽  
Billie C. S. Slater ◽  
Eric A. Nickel ◽  
Karl A. Koester ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lower Limb ◽  
Learning Algorithms ◽  
User Interaction ◽  
Standing Position ◽  
Machine Learning Algorithms ◽  
Measurement Unit ◽  
Smartphone App ◽  
Automatic Mode ◽  
Va Health Care System

Most commercially available lower-limb prostheses are designed for walking, not for standing. The Minneapolis VA Health Care System has developed a bimodal prosthetic ankle-foot system with distinct modes for walking and standing [1]. With this device, a prosthesis user can select standing or walking mode in order to maximize standing stability or walking functionality, depending on the activity and context. Additionally, the prosthesis was designed to allow for an “automatic mode” to switch between standing and walking modes based on readings from an onboard Inertial Measurement Unit (IMU) without requiring user interaction to manually switch modes. A smartphone app was also developed to facilitate changing between walking, standing and automatic modes. The prosthesis described in [1] was used in a pilot study with 18 Veterans with lower-limb amputations to test static, dynamic, and functional postural stability. As part of the study, 17 Veterans were asked for qualitative feedback on the bimodal ankle-foot system (Table 1). The majority of participants (82%) expressed an interest in having an automatic mode. The participants also indicated that the automatic mode would need to reach walking mode on their first step and to lock the ankle quickly once the standing position was achieved. When asked about how they wanted to control the modes of the prosthesis, 82% wanted to use a physical switch and only 12% wanted to use a smartphone app. The results indicated that the following major design changes would be needed: 1) A fast and accurate automatic mode 2) A physical switch for mode changes This paper describes the use of machine learning algorithms to create an improved automatic mode and the use of stakeholder feedback to design a physical switch for the bimodal ankle-foot system.

scholarly journals The Efficiency of Social Network Services Management in Organizations. An In-Depth Analysis Applying Machine Learning Algorithms and Multiple Linear Regressions

2020 ◽  
Vol 10 (15) ◽  
pp. 5167 ◽  
Author(s):  
Luis Matosas-López ◽  
Alberto Romero-Ania
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Gender Equality ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Network Services ◽  
Content Increase ◽  
Multiple Linear Regressions ◽  
Social Network Services ◽  
Linear Regressions

The objective of this work is to detect the variables that allow organizations to manage their social network services efficiently. The study, applying machine learning algorithms and multiple linear regressions, reveals which aspects of published content increase the recognition of publications through retweets and favorites. The authors examine (I) the characteristics of the content (publication volumes, publication components, and publication moments) and (II) the message of the content (publication topics). The research considers 21,771 publications and thirty-nine variables. The results show that the recognition obtained through retweets and favorites is conditioned both by the characteristics of the content and by the message of the content. The recognition through retweets improves when the organization uses links, hashtags, and topics related to gender equality, whereas the recognition through favorites increases when the organization uses original tweets, publications between 8:00 and 10:00 a.m. and, again, gender equality related topics. The findings of this research provide new knowledge about trends and patterns of use in social media, providing academics and professionals with the necessary guidelines to efficiently manage these technologies in the organizational field.

Machine learning and geophysical inversion — A numerical study

2019 ◽  
Vol 38 (7) ◽  
pp. 512-519 ◽  
Author(s):  
Brian Russell
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Numerical Study ◽  
Learning Algorithms ◽  
Mathematical Structure ◽  
Machine Learning Algorithms ◽  
Reflection Coefficients ◽  
Geophysical Inversion ◽  
The Difference ◽  
Tuning Frequency

As geophysicists, we are trained to conceptualize geophysical problems in detail. However, machine learning algorithms are more difficult to understand and are often thought of as simply “black boxes.” A numerical example is used here to illustrate the difference between geophysical inversion and inversion by machine learning. In doing so, an attempt is made to demystify machine learning algorithms and show that, like inverse problems, they have a definite mathematical structure that can be written down and understood. The example used is the extraction of the underlying reflection coefficients from a synthetic seismic response that was created by convolving a reflection coefficient dipole with a symmetric wavelet. Because the dipole is below the seismic tuning frequency, the overlapping wavelets create both an amplitude increase and extra nonphysical reflection coefficients in the synthetic seismic data. This is a common problem in real seismic data. In discussing the solution to this problem, the topics of deconvolution, recursive inversion, linear regression, and nonlinear regression using a feedforward neural network are covered. It is shown that if the inputs to the deconvolution problem are fully understood, this is the optimal way to extract the true reflection coefficients. However, if the geophysics is not fully understood and large amounts of data are available, machine learning can provide a viable alternative to geophysical inversion.

Statistical Study of Machine Learning Algorithms Using Parametric and Non-Parametric Tests

2020 ◽  
Vol 11 (3) ◽  
pp. 80-105 ◽  
Author(s):  
Vijay M. Khadse ◽  
Parikshit Narendra Mahalle ◽  
Gitanjali R. Shinde
Keyword(s):  
Machine Learning ◽  
Optimal Algorithm ◽  
Smart Cities ◽  
Learning Algorithms ◽  
Performance Measure ◽  
Machine Learning Algorithms ◽  
Training Data ◽  
Statistical Validation ◽  
The Difference ◽  
Validation Of Results

The emerging area of the internet of things (IoT) generates a large amount of data from IoT applications such as health care, smart cities, etc. This data needs to be analyzed in order to derive useful inferences. Machine learning (ML) plays a significant role in analyzing such data. It becomes difficult to select optimal algorithm from the available set of algorithms/classifiers to obtain best results. The performance of algorithms differs when applied to datasets from different application domains. In learning, it is difficult to understand if the difference in performance is real or due to random variation in test data, training data, or internal randomness of the learning algorithms. This study takes into account these issues during a comparison of ML algorithms for binary and multivariate classification. It helps in providing guidelines for statistical validation of results. The results obtained show that the performance measure of accuracy for one algorithm differs by critical difference (CD) than others over binary and multivariate datasets obtained from different application domains.

scholarly journals The Difference of Machine Learning and Deep Learning Algorithms

2021 ◽  
Author(s):  
Yew Kee Wong
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Model Building ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Human Intervention ◽  
The Difference

In the information era, enormous amounts of data have become available on hand to decision makers. Big data refers to datasets that are not only big, but also high in variety and velocity, which makes them difficult to handle using traditional tools and techniques. Due to the rapid growth of such data, solutions need to be studiedand provided in order to handle and extract value and knowledge from these datasets. Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Such minimal human intervention can be provided using big data analytics, which is the application of advanced analytics techniques on big data. This paper aims to analyse some of the different machine learning algorithms and methods which can be applied to big data analysis, as well as the opportunities provided by the application of big data analytics in various decision making domains.

scholarly journals Examination of Assorted Social Engineering Attack by Different Types of Machine Learning Algorithms

2021 ◽  
Vol 10 (7) ◽  
pp. 56-60
Author(s):  
Amal Alhamad ◽  
Dalal Aldablan ◽  
Raghad Albahlal
Keyword(s):  
Machine Learning ◽  
Social Network ◽  
Decision Tree ◽  
Learning Algorithms ◽  
Social Engineering ◽  
Machine Learning Algorithms ◽  
Privacy And Security ◽  
Different Types ◽  
Tree Algorithms

The most powerful attack on the systems is Social Engineering Attack because of this attack deals with Psychology so that there is no hardware or software can prevent it or even can defend it and hence people need to be trained to defend against it.[1] Social engineering is mostly done by phone or email. In this research, which is based on previous research we have conducted, the aim of it was of it was to highlight the different social engineering attacks and how they can prevent in social network because social engineering is one of the biggest problems in social network, a concern the privacy and security. This project is using a set of data then analysis it uses the Weka tool, to defend against these attacks we have evaluated three decision tree algorithms, RandomForest, REPTree and RandomTree. It was also related to an J48 algorithm, On the contrary, here contains a complete overview of social engineering attacks, also more than one algorithm was searched.

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