Reviewerly/exHarmony huggingface dataset:
Exploration on the reform path to Talent Training Mode of Industry-Education Integration for Finance and Economic Majors under the background of Big Data

In the big data era, data as an important production factor, has penetrated into every functional field of industry and business. Based on the background of the development of digital economy, how to prepare for the new form and direction, how to innovate and reform the practical teaching mode to meet the new requirements of the development of digital economy and society is an important problem to be solved in the process of talent training in economic colleges and universities. Combined with the new changes in the demand side of economic talents, this paper analyzes the problems existing in the talent training mode of colleges and universities from the education supply side, and takes the integration of industry-education as a breakthrough, from the following aspects, this paper studies the reform path of the talent training mode of integration of industry and education, including the redesign of the talent training scheme in colleges and universities, the joint design of the course teaching system, the supplement of the practical teaching curriculum content of big data in economy, the construction of teaching scene under digital economy and the co-construction of knowledge sharing platform.
Reform and Practice of Discrete Mathematics Course Based on Fine Examination

Discrete mathematics is the basic course of computer major. This paper aims at the present situation of discrete mathematics teaching, the design scheme of course content, teaching process and course examination based on fine examination is put forward. And through the CG platform, students' pre class test, homework submission, stage test, platform exchange and interaction and other teaching processes are effectively managed. Through the data analysis of the teaching process for more than three years, it can be concluded that the program effectively improves the learning effect of students, improves the teaching ideas and methods of teachers, and improves the level of teaching quality. It is worth popularizing widely in the teaching of discrete mathematics of computer major in Colleges and universities.
Perfect ZK Argument of Knowledge of Discrete Logarithm in A Cyclic Group with Unknown Order

ZK (zero knowledge) proof of knowledge of discrete logarithm (and sometimes extended to ZK proof of equality of discrete logarithms) in cyclic groups with unknown orders are widely employed in various cryptographic applications. To the best of our knowledge the present implementations of these two proofs have some drawbacks. Firstly, they can only achieve statistical ZK, which is not only weaker in theory than perfect ZK but also difficult to formally prove in practice. Moreover, the drawback is not limited to theoretic problems like provability but sometimes deteriorate efficiency of ZK proof to an intolerable level as we will show in a case study. The first perfect ZK argument of the proof is proposed in this paper, which is formally provable and can always guarantee acceptable efficiency. It is especially suitable for applications with high requirement on privacy and complex secure protocols requiring concise and formal proof of ZK privacy.
A Glimpse of Pedagogical Impacts of Social Media: Mirror, Mirror on the Wall, Who Is the Fairest of Them All?

Etiquette Education and the use of social media for communication are key elements for learning, especially when the modern networking so prevail. The pedagogical purpose of this paper is to report on an investigation of user’s behaviors on Instagram. Answers were sought to questions such as: Do users use Instagram? What genres do they browse? Are there any inappropriate posts seen on the Instagram? What solutions do users know to resolve the inappropriate posts on the Instagram? And finally, how teachers can be involved in scaffolding or facilitating etiquette development? The study demonstrates how etiquette is ‘behaved’ in social media such as Instrgram.com. Given the fact that it seems politeness and behavior development has been missing in a digital age, what might be the best possible way of integrating etiquette education in the college English curriculum? The study discriminates among 512 comments and remarks from various ranges of users. Issues relating to awareness of bad conduct on social media platforms and whether social media users filter what they follow, and FOMO (as The Fear Of Missing Out) are tackled. How the phenomenon of comparison is the thief of joy in social media? How leaving nasty comments while hiding behind a screen and an anonymous username appears to be the commonest practice in using social media? How body image concerns the viewers? And how social media takes a toll on society? It is suggested that Etiquette teaching needs to be extended into the standardized English curriculum at university level.
Further Related Work

Cryptocurrencies are highly volatile assets, part of their volatility can be explained by news articles and more particularly the tone or sentiment of this articles. Moreover, more trading in cryptocurrencies also influences the amount of news. Furthermore, cryptocurrencies have a lot of periods where the price significantly deviates from its fundamental value, also known as bubbles. In this chapter, the reader can find an overview of news impact on cryptocurrencies and a thorough overview of bubble behavior.
Blockchain Technology: A Brief Overview

Blockchain technology and distributed ledger has attracted massive attention and has triggered multiple projects in different industries. Blockchain is one of the most important technical invention in the recent years. It serves as an immutable ledger which allows transactions to take place in a decentralized manner. Blockchain based applications are springing up and are covering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability and security problems waiting to be overcome. This paper presents a comprehensive overview on blockchain technology.
Region-aware Registration for Multi-stained Histology Images

Multi-stained histology images can provide a wealth of pathological information for clinical diagnosis. Accurate registration of multi-stained images, especially on ROIs (e.g. tumor area), can offer a more precise and comprehensive understanding of a specific type of disease. However, the existing registration methods for histology images usually ignore the difference between ROIs and non-ROIs, which may lead to a biased result if the non-ROIs dominate the registration process. To address this problem, we propose a region-aware registration method for the multi-stained stained histology images. The proposed method consists of three main components: 1) pre-alignment to eliminate the significant rotation and displacement between images, 2) U-Net for ROIs extraction, 3) Region-aware non-rigid multi-modal registration framework to register whole image and ROIs simultaneously. We evaluate our method on the publicly available ANHIR challenge dataset. The qualitative and quantitative results show that our registration method outperforms other methods on the ROI level while keeping similar registration accuracy with other methods on a whole image level.
Descriptive Geometry in the Time of COVID-19: Preliminary Assessment of Distance Education During Pandemic Social Isolation

The pandemic forced a change in the teaching of descriptive geometry, a basic subject of many engineering fields. To conduct classes during COVID-19, the following resources were used: the ELF platform, MS Teams, SketchUp, AutoCAD, email. It was decided to prepare the electronic textbook along with geometric problems to be solved, supplemented by on-line consultations with students. Despite difficulties (technological, psychological), the results obtained by students during the pandemic were very good. Although the average grade during distance learning was higher than in the case of traditional education, students reported a clear need for direct contact with the teacher. © 2020
Cognivitra: An Information Technology-Based Solution to Support Cognitive and Physical Training at Home

Objectives: this article focuses on an information technology-based application, the Cognivitra, to support cognitive and physical training (i.e., dual-task training), which can be done at home with supervision of a health care provider. Cognivitra aims to increase the number and duration of training sessions and to overcome restrictions imposed by the availability of human and physical health care resources, and the aim of the study reported by this article was the assessment of the respective functional prototype. Methods: an observational study was conducted to assess the functional prototype using a peer review approach. The prototype was exposed in a highly specialized event related to healthy and active ageing, and key stakeholders were invited to test it and share their insights, including expectation of use, perception of market value, suggestions for improvement and other comments. Results: the observational study was conducted with 57 specialists in information technology-based applications to support healthy and active ageing, with an average age of 41.5 years. The results indicate that most of the participants would use or suggest the utilization of Cognivitra. Moreover, the participants made positive comments and improvement suggestions that focused on the difficulty level, the system response performance, the intuitiveness of icons and instructions, the user interaction, and the exercise duration. Conclusion: in general participants considered that Cognivitra is a useful tool and may represent an added value for remote dual task training. Although the results of this study were very positive and encouraging, improvements should be implemented before starting tests with real users.
ImageFuse: A Multi-view Image Featurization Framework for Visual Question Answering

Visual Question Answering (VQA) is a task where machines are challenged to produce correct answers for a question asked about an image. This paper proposes a novel image featurization framework named ImageFuse to improve the task of VQA. It implements a combination of feature fusion networks to form a fine-grained image representation instead of directly adopting common representations from the popular ImageNet CNN models via transfer learning. The two parallel fusion networks are trained using Canonical Correlation Analysis (CCA) and Autoencoders (AE) to capture both linear and non-linear relationships that exist in multiple views of the image. Extensive experiments conducted on DAQUAR VQA dataset show a significant improvement for the proposed framework over single image representation based VQA systems.
Assessment of Machine Learning Security: The Case of Healthcare Data

With technological advances and the use of the Internet everywhere, And the widespread use of machine learning has become important to pay attention to security in all areas of life, especially in the healthcare field, many concerns have arisen regarding the security of patient confidential data in health systems. As it became possible to change patient data, which would lead to a change in data accuracy or to data theft, which would lead to a violation of the safety system in the field of health care. In this paper, a health system was studied in a hospital in Jordan after collecting information on 769 records for pregnant diabetics. The analysis used Python to test the accuracy of this information and improve the performance of the model being created using machine learning algorithms, including decision trees and random forests. Since patient information in any health system has been exposed to many threats and weaknesses, the main goal was to reduce them, and obtain accurate information with good performance and excellent quality, to avoid compromising health rights and data protection for patients.
Deep Learning for Copyright Protection

“One belt, one road” strategy cannot do without language service, and copyright problem of big data is prominent. One belt, one road, one belt, one road, one belt, one road, one belt, one road, is analyzed in this paper. The copyright object category of “one area and one road” language data under cloud environment is analyzed, and the category of “one area and one way” language data copyright risk under cloud environment is summed up.
Human-in-the-Loop Flight Training of a Quadcopter for Autonomous Systems

A software framework was developed connecting a Parrot AR 2.0 quadcopter to a full motion flight simulator at the Michigan Aerospace Center for Simulations. The combination of a drone with a flight simulator provides for precise remote operations without putting a human pilot at risk. We use the motion capabilities of our flight simulator to keep the pilot oriented consistently with the quadcopter. The result was a responsive system utilizing telemetry data to synchronize a flight simulator to a drone’s movement with low latency. The proposed system was developed over the course of 30 weeks and put through its paces in our lab over two days. This paper outlines our methods, from the software architecture, to a detailed description of the hardware, to accomplish this aim and outlines some directions for further study.
Persian Opinion Mining:A Networked Analysis Approach

Currently, Opinion Mining is a field of great interest and development since it has various practical applications in different fields. The Persian language has a great deal of potential for thorough investigation in various characteristic of natural language processing. In this study, we review the latest studies in Persian Natural Language Processing and introduce modern Deep Learning models. In the following, we express the most important Sentimental Analysis Challenges in Persian ancient language and demonstrate related Persian Information Retrieval Tools, Models, Libraries, and Techniques. Finally, we create the Network of Research Centers, Privately Industrial Companies, and their products in Persian Natural Language Processing. The utilization of the Network Science approach in the field gives us a comprehensive big picture of the latest advancement and development in the Persian Sentiment Analysis topic and identifying the Blind spots.
A Load Balancing Based Cost-Effective Multi-tenant Fault Tolerant System

With every single day the data over the internet is growing exponentially and it is becoming the need of the hour to manage and store this immense data efficiently. The multi-tenant architecture techniques are becoming quite beneficial for handling large databases and so are the cloud-based services. Also, with the hasty increase in the number of users in health-based platforms over the internet, the growth of data stores over this category has become a major concern. This paper proposes a multi-tenant system that has been implemented using a column-based NoSQL database, Cassandra, and the useful features of the webservices. Another, major apprehension while handling such huge data is scalability, the ease of handling tenant’s enormous dynamic data. To solve this, the proposed solution uses an elastic load balancer that automatically distributes incoming application traffic across multiple targets, such as Amazon EC2 instances, containers, IP addresses, Lambda functions, and virtual appliances. Apart from this, there is also a need to tackle the software faults and system failures to ensure high availability of the system. This problem is handled by making the system fault-tolerant in the proposed multi-tenant architecture.
Book Rating Model Based on Self-Attention and LSTM

With the rapid development of Internet technology and online communication communities, text data has exploded. Emotion analysis of network information and research on the emotional tendency of users in various industries for products have become important research topics. Neural networks have good performance in the field of natural language processing. The traditional recurrent neural network has the problem of gradient disappearance. Therefore, this paper combines Self-Attention mechanism and LSTM model to realize the multi-classification of text emotional attributes and effectively obtain complete long sequence information. The experiment uses the Goodreads book review dataset for sentiment analysis. The experimental results show that the Self-Attention + LSTM model has a higher prediction accuracy than RNN. This proves that the model proposed in this paper can be used to improve the accuracy of text sentiment classification and has certain research value.
Fast Click-Through Rate Estimation Using Data Aggregates

Click-Through Rate estimation is a crucial prediction task in Real-Time Bidding environments prevalent in display advertising. The estimation provides information on how to trade user visits in various systems. Logistic Regression is a popular choice as the model for this task. Due to the amount, dimensionality and sparsity of data, it is challenging to train and evaluate the model. One of the techniques to reduce the training and evaluation cost is dimensionality reduction. In this work, we present Aggregate Encoding, a technique for dimensionality reduction using data aggregates. Our approach is to build aggregate-based estimators and use them as an ensemble of models weighted by logistic regression. The novelty of our work is the separation of feature values according to the value frequency, to better utilise regularization. For our experiments, we use the iPinYou data set, but this approach is universal and can be applied to other problems requiring dimensionality reduction of sparse categorical data.
Flexible Rectification of a Speckle Projection System for Depth Sensing

Rectification is a critical process for speckle projection systems to ensure reliable image matching in producing a depth map. However, few studies have discussed it up to the present. In this article, we present a flexible but robust approach to address this issue. It mainly consists of two steps: calibration and rectification. The calibration step takes one or several pairs of images of the projected speckle pattern on a plane to estimate a unit vector indicating the direction of the line where emitter center lies on. The rectification step virtually rotates the camera based on this vector to obtain rectified images. Given camera intrinsic parameters, this approach only needs a minimum configuration consisting of two freely captured speckle images of a planar surface to complete system rectification, which makes it particularly suited for the case with no access to the internal projected speckle pattern. Elaborate experiments on real speckle images and synthetic images from different camera poses are conducted, demonstrating the effectiveness and robustness of the proposed approach. Furthermore, we also demonstrate that when the internal pattern is recovered from the rectified images, it reaches an accuracy comparable with the ones from the best state-of-the-art methods in performing a stereo matching, but with a much simpler operation to obtain.
Autonomous mobile robot development based on duckietown platform for recognizing and following the traffic sign

Abstract This paper presents development of an autonomous mobile robot which can recognize the traffic sign and move according to that recognized sign. The mobile robot was developed based on Duckietown platform, which is a research which focuses on self-driving vehicles. The mobile robot was equipped with a fish eye camera as its vision device and raspberry pi.3 controller module as the main controller for doing image processing in order to recognize the signs and controlling the movement of the robot. The program runs on ROS platform. The system has been tested by moving mobile robot through all area and the results showed average recognition rate of 80%. The system also has been tested in variation of light intensity and the result showed average recognition rate of 80%. Another test also was done by adding disturbance object which has similar or same color as sign color and the result showed average recognition rate of 43.3%.
Extraction Method of Machine Translation Equivalent Pairs in Chinese-English Comparable Corpus based on Text Mining

The principle of the corpus-based method is to use statistics and probability methods to build a control corpus containing various sentence patterns. During the translation, example sentences similar to the input sentence are extracted from the corpus., hence, extraction method of the machine translation equivalent pairs in the Chinese-English comparable corpus based on text mining is constructed in this paper. Firstly, the proposed research work has analyzed the theoretical basis of text mining by combining the pattern recognition models, neural networks and fuzzy model to effectively collect the features. Then, the machine translation platform is implemented. Through the testing on the accuracy, the performance of the proposed method is studied.
A formal framework for robot to understand compound concepts

Abstract Social robots are playing an important role in assisting humans in shopping, caring for the elderly and educating children. However, to accomplish these tasks with high quality, social robots still face huge challenges. One of the main challenges is how robots understand the intentions of human behavior and predict human needs in order to respond appropriately. Most existing social robots determine the intentions and needs of humans based on the postures and actions in a specific scene. We know that it is not enough to understand human intentions and needs based on gestures and actions. It will be more perfect to understand human intentions and needs through natural language communication. To this end, we propose a formal framework for describing robots’ understanding of simple and compound concepts. Once the robot has the ability to understand compound concepts, it has the ability to understand some simple knowledge. We first introduce the formal methods of expressing the connotation and extension of simple concepts, and then propose a framework for robots to understand simple concepts, and finally build a formal framework for robots to understand complex concepts. This framework provides a formal method for robots to understand knowledge.
Saturation High Light Removal of Pressure Plate Image Based on Feature Matching

When the artificial intelligence inspection robot collects the image of the relay protection pressure plate, the texture of the pressure plate image will be missed due to the reflection of the glass cabinet door. In order to solve this problem, a saturation high light removal method of the pressure plate image based on feature matching is adopted. The pressure plate image with saturation high light should be analyzed first, and two photos with different positions of saturation high light are taken from different angles of the same scene at the same time. Considering the different shooting angles, the SIFT algorithm is used to match the features and perspective transformation is used to make the angle of view consistent. Then the Canny operator is used in the edge detection of the saturation high light region, and the corresponding position of the auxiliary image can repair the texture of the reference image. The experimental verification example is proposed to prove the rationality and effectiveness of this method.