Reviewerly/exHarmony huggingface dataset:
A Visual Introduction to Machine Learning, AI Framework, and Architecture

Every standardized task that is done by applying computer tools was once performed low level in bulk for the field of computer science. People who were highly ambitious and had technical skills got answers using artificial intelligence. They believed that in the future they would be equipped with artificial intelligence tools and technology. Now, artificial intelligence is commonly used for day-to-day work routines. Artificial intelligence consists of machine learning and deep learning. Computers can act based on algorithms designed and trained to them; it is an art of science to train computers by this type of learning. Progression toward human brain-level machine learning plays a major role in making such algorithms for computers. Deep learning is a sub-category of machine learning where artificial neural networks inspire these algorithms to produce results as predicted. Supervised deep learning desired results are based on implementation of labeled data and required algorithms. Transformation of nonlinear input data is used to generate a model as output.
Security in Cloud Computing using IDS

The market growth is primarily focusing on the implementation and investment in various cloud based projects as the rise in demand for cloud based platforms has been a benefit for cloud based vendors creating opportunities in expansion of the business by launching various editions and advanced version for better productivity. The global cloud computing market is projected to grow from $250.04 billion in 2021 to $791.48 billion in 2028 at a CAGR of 17.9% in the forecast period The growth is attributable to the digital transformation, increasing penetration towards connected devices and growing automation amongst small and medium enterprises. Unforeseen emergencies, application vulnerabilities and online cyber attacks are the various restraining factors for Privacy &amp; Security Concerns about data breaches and data losses. With the requirement of faster processing cycle in real time scenario, various enterprises are moving towards Omni-Cloud Solutions to leverage numerous advantages such as ease of data use, effective decision making , scalability and security of data. The trending protective method applicable here is Intrusion Detection System, which is one the way for real-time detection for breaches and establishment of more stronger security.
Analisis Kebutuhan Sistem Informasi Pembelajaran Daring pada Lab Bank Syariah Politeknik Negeri Bandung

The main purpose of this study is to analyze the needs for the development of Islamic Bank Lab learning media. in Politeknik Negeri Bandung. The stages of research on needs analysis of teaching media development carried out were: data collection, data analysis and document review, identification of actors, and making use case diagrams. The analysis was carried out on the current system using PIECES analysis. The results of the PIECES analysis are made in tabular form. In addition, a functional and non-functional requirements analysis was also carried out. Identification of actors who will play a role in teaching media is described in the form of use case diagrams. The results of the PIECES analysis show that a system is needed that can accommodate practical learning in the lab. Islamic banks that can be accessed by students from anywhere in real time, fast and easy to use by all actors involved. Therefore, it is necessary to develop an information system as a learning medium in the Islamic Bank lab. Politeknik Negeri Bandung.
An interactive approach to text browsing based on anchor location

Due to the rapid advancement of informationization process, a large number of product characteristic data have been accumulated in the process of testing, and the quantity of product characteristic data is increasing exponentially. The traditional characteristic data often presents the data content in the way of query, etc., and fails to display the data existing in the target rapidly and integrally. This paper designs an anchor point positioning based on the text through interactive method, used in a web page display product feature information in the form of text, use convenient interaction provides users with convenient, fast text browsing experience, elaborated the method of technical implementation ways, finally proved that the method enables users more intuitive preview, Improve the efficiency of viewing and analyzing data.
Beyond federated learning: On confidentiality-critical machine learning applications in industry.

Abstract Federated machine learning frameworks, which take into account confidentiality of distributed data sources are of increasing interest in smart manufacturing. However, the scope of applicability of most such frameworks is restricted in industrial settings due to limitations in the assumptions on the data sources involved. In this work, first, we shed light on the nature of this arising gap between current federated learning and requirements in industrial settings. Our discussion aims at clarifying related notions in emerging sub-disciplines of machine learning, which are partially overlapping. Second, we envision a new confidentiality-preserving approach for smart manufacturing applications based on the more general setting of transfer learning, and envision its implementation in a module-based platform.
Expression Reognition Survey through Multi-Modal Data Analytics

In the field of computer vision, capturing human expression from a video is essential for a variety of time-sensitive applications including driver safety and education. Because there are various models of expression (e.g., speech, face, gesture, etc. ), Human Expression Recognition can be done in a variety of methods. In this work, we look at various strategies for recognizing facial expressions. We primarily concentrated on two models in this review: speech signal and facial image. The entrance survey is conducted in two steps, according to the generic approach of human expression recognition. Our feature extraction approaches were divided into two categories: audio features and facial features. The review found different classifiers helped to decide the best framing of the markov chain for a model. We also looked at certain multi-modal expression recognition algorithms that used the multi-modal notion at various stages, such as feature fusion and decision fusion. We also looked at several databases that have been used by previous studies in addition to these approaches. A full-fledged comparison is also provided to completely show the review.
Reducing Human Error in Online Controlled Experiments: A case study at ING

Online controlled experimentation (OCE), also called A/B testing, is an often used tool in industry to determine if deploying changes into production is the right decision to make. Running experiments has shown an immense impact to the revenue of companies in industry, however this type of experimentation comes with a lot of pitfalls, of which some that can invalidate the entire experiment. This thesis describes the impact these pitfalls have on the work of experimenters at ING, a global bank, by performing informal interviews with practitioners and performing a survey with 52 participants. Next, building on existing solutions, a set of solutions is proposed to solve these pitfalls. To determine if these solutions solve the problem and will help the experimenter, these solutions are validated in the same survey. This thesis shows that experimenters are well informed about the existence of pitfalls and believe that almost all should be resolved, with the exception of competitor safety, which is believed to not be important. There are many promising solutions to these pitfalls which experimenters rate as helpful. The best rated solution was ”Enforcing the correct experiment duration”. Almost all respondents perceived the solution to (slightly) help the experimenter in performing their experiments. Finally, this thesis creates a roadmap for evaluating these solutions in a real-world scenario.
Teaching physical chemistry: Electronic labs

Information technologies used in the teaching of such fundamental disciplines as physical chemistry, colloidal chemistry include: electronic educational complexes, electronic teaching aids, lectures, educational films; electronic laboratory and practical works, tests, reference books. As part of the disciplines under consideration, complexes of electronic laboratory works were developed at the Department of Physical and Colloidal Chemistry of B.N. Yeltsin Ural Federal University. The advantages of electronic works over traditional ones are shown. These are opportunities to “work” with chemicals and “use” devices that are not allowed for safety in a student workshop; possibility of strengthening the student’s independence, abandoning the team method of performing laboratory work; possibility of remote operation. As a result of the work, the basic principles of working with the Microsoft Excel program when creating electronic laboratory works are formulated. It is emphasized that it is important to leave a leading role in electronic laboratory work to a student. During the development of electronic laboratory works, stages are formed where a student independently enters data, reads numerical values from experimental dependencies, etc. The performance of these methodological techniques leads to an increase in the activity of cognitive activity of students.
Blockchain: A Possible Alternative to Achieving Health Information Exchange (HIE)

A patient may have access to one or more healthcare organizations, meaning that their health information is spread across separate EHR systems, resulting in data fragmentation. Thus, the medical history of a patient may not be complete, consistent or comprehensive if the records cannot be combined into a single consistent overview that may not be efficient for the future diagnosis of the patient. Health Information Exchange (HIE) relates to the electronic transfer of health care information between institutions and practitioners within a specific group, region or hospital. However, HIE is surrounded with various challenges such as data insecurity, threat to patient privacy, dependency on third parties and so on. Block-chain through its characteristics such as safety, privacy, decentralization, and immutability has the ability to transform health care, putting the patient at the core of the health care ecosystem, and enhancing health data security, privacy, and interoperability thereby offering HIE with excellent potential. In this paper Health Information Exchange (HIE) and Block-chain Technology are discussed and the possibility of using block-chain technology to achieve HIE is presented.
Research on Computer Application Software Teaching in Colleges and Universities Based on Virtual Cloud Computing

This paper mainly studies the teaching of computer application software in colleges and universities based on virtual cloud computing. By analyzing the essence of cloud computing and the problems existing in the current computer application software teaching, this paper analyzes and discusses the specific application of cloud computing in teaching. This paper discusses from four aspects: Building relevant platforms to realize the integration and sharing of teaching resources, applying virtual cloud computing to innovate teaching forms, improving teachers’ ability to use virtual cloud computing for teaching, and operating cloud computing to ensure teaching equipment and information security, showing the advantages and significance of virtual cloud computing for computer application software teaching in colleges and universities.
LibGuides: Toolbox for Authors: Writing and Sharing Your Research: Home

Looking for tips and resources for writing? Explore these suggestions from the WBG Library staff. (Where noted, licensed resources and Intranet pages are available for WBG or IMF staff only. Some suggestions may require additional licensing)
ADJUSTMENT OF LARGE SOLAR OVEN FLAT HELIOSTAT HELIOSTATS

This paper presents an improved method for adjusting individual mirrors –facets of heliostats of a Big Solar Furnace (BSF) with a heat output of 1000 kW in Uzbekistan. Due to the fact that a BSF consists of 22,790 pieces of individual mirrors, the adjustment - setting a specific geometric position of these mirrors is very important. The process of adjusting the mirrors is very time consuming and lengthy. Often exactly the adjustments are influenced by subjective factors by the aligners. In order to improve the performance and accuracy of the alignment, the facet of heliostats has applied the Technical Vision System (TVS), as well as improved the process of assessing the state of alignment and the processing of alignment data. The TVS consisting of a video camera, an interface, a personal computer and special software allows you to visually and accurately assess the alignment conditions of the heliostat facets before and after the alignment process. Allows you to save the data in computer memory for further processing and analysis. Allows you to create a database of the alignment status of each of the 62 heliostats of LSF. Special software developed by us allows you to quickly and accurately determine the deviations of the heliostat facets from the calculated geometric points in angular minutes. Based on the data obtained, you can build histograms, graphs, etc. for visual analysis of the heliostat alignment states before and after the alignment process.
FACE RECOGNITION TECHNOLOGY AND FACE MASK DETECTION USING DEEP LEARNING MODEL

Face recognition technology may be a biometric technology that is predicated on the identification of face expression of a person. Folks collect the face images, and therefore the recognition instrumentality mechanically processes the images. The paper describes the event stages and the connected technologies of face recognition and social distancing. We have a tendency to introduce the analysis of face recognition for real conditions, and that we introduce the overall analysis standards and the general databases of face recognition. We have a tendency to provide an innovative read of face recognition and therefore the projected technique detects the face from the image properly then identifies if it's a mask thereon or not. As a police investigation task performer, it can even find a face in conjunction with a mask in motion. The tactic attains accuracy up to 95.77% and 94.58% severally on 2 completely different datasets. We have a tendency to explore optimized values of parameters by exploiting the successive Convolutional Neural Network model to detect the presence of masks properly while not inflicting over-fitting. The paper presents a technique for social distancing detection using deep learning to judge the gap between folks to mitigate the impact of this coronavirus pandemic. The detection tool was developed to alert people to take care of a secure distance with every other by evaluating a video feed. The video frame from the camera was used as input, and the ASCII text file object detection pre-trained model supported the YOLO v3 algorithmic program was used for pedestrian detection. Later, the video frame was remodeled into a top-down read for distance measuring from the second plane. The gap between people will be calculable and any noncompliant combination of individuals within the show will be indicated with a red frame and red line. The projected technique was valid on a pre-recorded video of pedestrians walking on the street. The result shows that the proposed method is in a position to determine the social distancing measures between multiple folks within the video and if the mask isn't detected in the video frame, with the face recognition and trained model we have a tendency to determine the person's name. The developed technique will be additional developed as a detection tool in real time application
Data Mining research of bachelor's degree dissertation of College of Electronic and electrical engineering in a university in China

This study tries to find out some rules and characteristics through the data mining of students' dissertation topics, such as the College of Electronic and Electrical Engineering, dissertation of undergraduate students majoring in Electrical Engineering and Automation in the past three years. The study employs the quantitative method of research. In this study, we will use the process of K-DD(Knowledge Discovery in Database). Using logical regression, it is to predict whether the student can reach the average level of students. Linear multiple regression was used to study the final score of students' graduation project depending on what factors. Through the method of decision tree, according to the basic level of each student and the subjects that he is good at, determine the direction of the paper topic that the student is suitable for. The conclusion of this paper shows that the main topics of the dissertation of Electrical Engineering and Automation are control, system design and simulation. The main software used is matlab. The results of logical regression and linear regression show that there is a sexual relationship between the final score of the thesis and the evaluation and defense performance of fellow teachers and instructors, and the final decision tree can also provide constructive guidance for the selection of the topic of the thesis.
E2-Train: Training State-of-the-art CNNs with Over 80% Less Energy

Convolutional neural networks (CNNs) have been increasingly deployed to Internet of Things (IoT) devices. Hence, many efforts have been made towards efficient CNN inference in resource-constrained platforms. This paper attempts to explore an orthogonal direction: how to conduct more energy-efficient training of CNNs, so as to enable on-device training? We strive to reduce the energy cost during training, by dropping unnecessary computations, from three complementary levels: stochastic mini-batch dropping on the data level; selective layer update on the model level; and sign prediction for low-cost, low-precision back-propagation, on the algorithm level. Extensive simulations and ablation studies, with real energy measurements from an FPGA board, confirm the superiority of our proposed strategies and demonstrate remarkable energy savings for training. Specifically, when training ResNet-74 on CIFAR-10, we achieve aggressive energy savings of >90% and >60%, while incurring an accuracy loss of only about 2% and 1.2%, respectively. When training ResNet-110 on CIFAR-100, an over 84% training energy saving comes at the small accuracy costs of 2% (top-1) and 0.1% (top-5).
Study on the influence of incubation temperature on specific protein detection results based on COMSOL simulation

Incubation temperature plays an important influential role in the detection process of specific proteins. In this paper, a specific protein detection device was designed and built by taking Lambert-Beer’s law as the basic detection principle. By using the photoelectric effect of the photoelectric sensor, the relative absorbance of the transmissibility sample was detected by the turbidimetric method, and then the substance content in the detected sample was quantitatively analyzed. Meanwhile, the increment PID algorithm was used to control the temperature in the incubation area, and then the finite element analysis method and COMSOL finite element simulation software were employed to analyze the temperature distribution and temperature variation trend in the incubation area of the reaction device. Besides, the microalbumin kit of Beijing Dandan Biotechnology was used to detect the influence of incubation temperature on the calibration curve of microalbumin (mALB) and its changing trend under different temperatures. The experimental results indicated that the increasing PID control algorithm could control the average temperature rise rate of the incubation area at 0.52 ℃/s and the temperature stability at &plusmn;0.1 ℃. When the incubation temperature was between 34℃ and 40℃, the change of temperature had no significant effect on the slope of the calibration curve. Within this temperature range, the linearity of the calibration curve gradually increased with the increase of the temperature. This study provided a basic experimental study on the effect of incubation temperature on the detection results of specific proteins.
TAG: Terraforming Mars

Games and Artificial Intelligence (AI) have had a tight relationship for many years. A multitude of games have been used as environments in which AI players can learn to act and interact with others or the game mechanics directly; used as optimisation problems; used as generators of large amounts of data which can be analysed to learn about the game, or about the players; or used as containers of content which can be automatically generated by AI methods. Yet many of these environments have been very simple and limited in scope. We propose here a much more complex environment based on the boardgame Terraforming Mars, implemented as part of the Tabletop Games Framework: a very large and dynamic action space, hidden information, large amounts of content, resource management and high variability make this problem domain stand out in the current landscape and a very interesting problem for AI methods of multiple domains. We include results of baseline AI game-players in this game and in-depth analysis of the game itself, together with an exploration of problem complexity, challenges and opportunities.
Socioeducational gaps derived from the impact of digitization in Spain 2020 point to a STEM and gender approach in education

The COVID-19 pandemic visualized how digital assets influence contemporary society and play a decisive role in economic recovery. The objective of this work is to identify the social and educational gaps derived from the impact of the level of digitization in Spain 2020. This research is based on comparative education as methodological, relational and critical with the technological implementation; who tries to link education with changes in their social environment; in order to contribute to the improvement of the educational system. In this sense, based on the DESI report for the last three years, the case of Spain is analyzed in relation to the average score of the rest of the EU member countries. The social gaps pointed to profiles of vulnerable populations for having few digital skills or for being practically excluded from digital services and resources, achieving a new perspective centered on the human being will mean achieving economic development. The educational gaps pointed to human capital and its low skills in software, few specialists in ICT specialties and low female representation in ICT specialties. These results showed the importance of STEM and gender-sensitive education, which appears to be the key to economic growth and national competitiveness.
Solving systems of ordinary differential equations using differential evolution algorithm with the best base vector of mutation scheme

A general algorithm is presented to approximately solve a great variety of systems of ordinary differential equations (ODEs) independent of their form, order, and given conditions. The systems of ODEs are formulated as optimization problem because it isn't an easy way to get the exact solution for systems of ODEs. Therefore, approximate solution is needed for solving systems of ODEs. One of the approaches used in this paper is using Fourier series expansion to approximate solutions of system of ODEs. The Differential Evolution (DE) algorithm, classified as a metaheuristic algorithm, is used as an optimization method to estimate the most accurate coefficients of Fourier series expansion. In this case, DE will be used to minimize the residual functions of the system of ODEs with Fourier series approximations. The original DE is made by putting the best base vector into the mutation part of the DE algorithm. The results show good performance of DE in solving system of ODEs.