Reviewerly/exHarmony huggingface dataset:
Application Data Analysis of FIF Oral Training System Platform

At the beginning of the New Year 2020, a sudden epidemic changed our learning and lifestyle. In order to guide and encourage contemporary college students to pass on the anti-epidemic spirit and positive energy that emerged from society during the epidemic, such as big love, gratefulness and responsibility, IFLYTEK company has opened a free smart teaching platform to universities across the country to inspire college students' enthusiasm for English learning during the period of "no class suspension" and to promote teaching methods. In the platform, the FIF oral training system can effectively help teachers and students of universities to carry out involving in oral English teaching activities. With the help of A.I.'s strength exercises, the participation in oral training has been greatly improved, and it has also provided students with a broader stage for English presentation. "The teaching mode supported by new information technologies such as artificial intelligence and big data will become the new normal form for university teaching.
Research on Application of Computer Technology in Petroleum Geophysical Exploration

Abstract With the development of society, people’s demand for oil is increasing. Petroleum is a necessary resource for mankind, and the difficulty of geological exploration for petroleum resources has increased. In the geological exploration of petroleum resources, using geophysical exploration technology can improve the extraction efficiency and the quality of oil resources, this reduces the waste of oil resources to a certain extent.
Conversation Engine for the Hearing and Vocally Impaired Using CNN

On our planet, Around nine billion individuals are hard of hearing and unable to speak. Correspondence between hard of hearing, quiet, and a typical individual has consistently been a difficult errand. Since ordinary individuals are not trained on gesture based communication, at the time of crisis passing on their message is troublesome. The human hand has always remained a popular choice to convey information in situations where other forms like speech cannot be used. Voice Conversion System (VCS) with Hand Gesture Recognition (HGR) and translation will be extremely valuable to have an appropriate discussion between an ordinary individual and a disabled individual in any language. This prescribed model intends to build up a framework that changes over the gesture-based communication into a human hearing voice in the ideal language to pass on a message to typical individuals, as well as converting speech into justifiable gesture-based communication for the tragically challenged. The Convolutional Neural Network (CNN) is used to train the model on different hand gestures and an app is built using Flask which uses this model. This application enables vocally impaired people to pass on their message using signs which gets changed over to human-understandable language and the speech is given as the result, as well as it translates the speech into hand gestures for the hearing impaired people.
An elementary linear-algebraic proof without computer-aided arguments for the group law on elliptic curves

The group structure on the rational points of elliptic curves plays several important roles, in mathematics and recently also in other areas such as cryptography. However, the famous proofs for the group property (in particular, for its associative law) require somewhat advanced mathematics and therefore are not easily accessible by non-mathematician. On the other hand, there have been attempts in the literature to give an elementary proof, but those rely on computer-aided calculation for some part in their proofs. In this paper, we give a self-contained proof of the associative law for this operation, assuming mathematical knowledge only at the level of basic linear algebra and not requiring computer-aided arguments.
Recognition of Abnormal Red Blood Cells Using Earth Mover's Distance Algorithm

Red blood cells (RBCs) play a significant role by carrying essential nutrients and oxygen throughout the body. These cells are shaped as biconcave discs that are flexible enough to pass through small capillaries easily. However, if these cells are irregularly shaped, they may reduce the oxygen-carrying capacity of the blood and have difficulty moving through the blood vessels. Blood tests are laboratory methods that are often performed by medical experts to manually determine abnormalities in the blood, but it may be subjective and susceptible to human error. Therefore, recognizing abnormal RBCs automatically through digital image processing would be beneficial. There are existing studies that focus on the application of different image processing techniques in recognizing abnormal RBCs automatically. However, they suffer from certain weaknesses such as: others did not cover all the abnormal RBCs, while the others still encountered a significant error in recognition due to the slim dissimilarities between the attributes that they used. In this study, the proponents sought to improve and provide solution for the limitations of the previous studies by proposing a system that can recognize 9 abnormal RBCs using a machine learning algorithm called Earth Mover's Distance Algorithm which allows partial matching between two different distributions. The test images used in the system are microscopic images of blood samples gathered from the web. The images gathered initially undergone image preprocessing to extract the necessary attributes from multiple regions of interests (ROIs) needed in the computation of the Earth Mover's Distance (EMD). EMD is the measure of dissimilarity between two multi-dimensional distributions in space. The lowest computed EMD represents the smallest dissimilarity of the input image to the template image. As a result, the system displays the total number and label of each abnormal RBCs recognized in a Graphic User Interface (GUI). The proposed system obtained an average reliability rate of 93.52%.
A Wearable Brain-Computer Interface Instrument with Aug- Mented Reality-Based Interface for General Applications

In the project we are demonstrating the combined usage Augmented Reality(AR) and brain faced com- puter interface(BI) which can be used to control the robotic acuratorby.Thismethod is more simple and more user friendly. Here brainwave senor will work in its normal setting detecting alpha, beta, and gam- ma signals. These signals are decoded to detect eye movements. These are very limited on its own since the number of combinations possible to make higher andmorecomplextaskpossible.Asasolutiontothis AR is integrated with the BCI application to make control interface more user friendly. Thisapplication can be used in many cases including many robotic anddevicecontrollingcases.HereweuseBCI-ARto detect eye paralysis that can be archive by detecting eyelidmovementofpersonbywearingheadbend.
Audio Quality Control Method Based on ASS (Audio Secret Sharing)

Audio Secret Sharing (ASS) can be used to provide security to audio channels by dividing the audio into smaller units called shares. With the advancement of technology and the availability of ubiquitous devices, a lot of audio platforms came into the picture that provides speeches, music, and other audio files which are usually guarded by a paywall. In cases where the low quality of the audio is provided for free and high-quality audio is guarded by a paywall, in that scenario the vendor stores two separate copies of the audio, one low and one high which certainly acquire twice space. The proposed scheme offers to store low-quality audio which can generate high quality with help of a share, this comes out to be space-efficient and we also don’t need to maintain two separate copies of the same audio. In the proposed scheme, an audio quality control method has been defined that can be used in modern audio platforms.
An MSVL-Based Modeling Framework for Back Propagation Neural Networks

With the rapid development and wide application of artificial neural networks, formal modeling and verification of their security become more and more significant. As a basic step towards the direction, this work proposes a comprehensive modeling framework for back propagation (BP) neural networks based on the formal language MSVL. In this framework, the structure and behavior of a BP neural network are formalized as specifications of data structures and operations, and they are in turn implemented as MSVL structs and functions, respectively. Based on the formalization, models of BP neural networks can be constructed and trained according to the requirements of users. Experimental results show that these models have good performance in terms of metrics concerning training and prediction such as loss and accuracy.
The Classification Model Sentiment Analysis of the Sudanese Dialect Used Into the Internet Service in Sudan

The analysis of Arabic opinions has not received much attention, such as English, primarily due to the challenges facing the elaboration of the complex Arabic language and the lack of tools and resources available to extract Arab sentiments from the text. This task is exacerbated when dealing with the Sudanese colloquial dialect that does not adhere to the formal grammatical structure of modern Standard Arabic. This study aims to analyze the opinions of the Internet service in Sudan written in the Arabic language using the modern Standard Arabic and Sudanese Standard Arabic accent, which was conducted on 1048 Facebook comments on the Internet service. The researcher used natural language processing techniques to process the data. Two different classifiers were applied: support vector machine (SVM) and Naive Bayes NB to classify comments based on their polarity either positive or negative. Then, the work was evaluated by four different measures as follows: accuracy, recall, accuracy, and measurement F. The results showed that SVM achieved the best accuracy—measurement which was 86.5% while NB achieved accuracy, which was equivalent to 80%.
WITHDRAWN: Utilization of filter for seepage phenomenon reduction under hydraulic structures

Water movement under hydraulic structures through a permeable layer of soil is considered as one of the main causes of failure of hydraulic structures. Therefore, this study aims to minimize the influence of water movement under hydraulic structures by using a filter. The case was simulated using the finite element analysis software ANSYS where many valuables were studied such as head of water, depth of upstream cut off wall, filter depth, and filter location. Such variables usually influence the uplift pressure, exit gradient and seepage discharge, which control the performance of the hydraulic structure. The results have shown that the position of filter at downstream leads to increases the seepage discharge and exit gradient, while at upstream, it reduces the uplift pressure significantly.
Research on Content Recommendation System of Army Equipment Information Network

Aiming at the actual needs of content recommendation of the Army Equipment Information Network, combined with the characteristics of users and content on the website, this article analyzed that the traditional collaborative filtering algorithm has poor recommendation results due to cold start and data sparse. A new algorithm which combined content-based recommendation algorithm with traditional collaborative filtering algorithm was proposed to solve this problem. The authors researched and designed a suitable model for the content recommendation of the website, and proposed a proper recommendation algorithm. Finally, the content of the website and the users' behavior data were used as experimental content to verify the validity of the proposed model algorithm. The results showed that the proposed recommendation algorithm could maintain a certain accuracy and fraction of coverage when the K value was at an appropriate value. The method met the personalized needs of users when browsing the website news.
Feature Selection and Polarity Classification Using Machine Learning Algorithms NB &amp;amp; SVM

Sentiment analysis and its classification of social data has become challenging now a days because of unstructured nature of data, slang, misspells and abbreviations used by customers while giving comments or reviews. Using machine learning approach for sentiment analysis helps in finding useful patterns and derive predictions which are important in decision making for improvement of overall products and customer satisfaction. In this paper we use tweets for famous mobile brands like Iphone, Vivo and Red MI. Machine learning algorithm like naïve Bayes and SVM are used to find polarity of tweets like positive, negative or neutral. This helps to find popular brands. Also we compare overall accuracy of these algorithms using measures like precision and recall and f measure.
Feature Reinforcement Learning: Part II. Structured MDPs

Abstract The Feature Markov Decision Processes ( MDPs) model developed in Part I (Hutter, 2009b) is well-suited for learning agents in general environments. Nevertheless, unstructured (Φ)MDPs are limited to relatively simple environments. Structured MDPs like Dynamic Bayesian Networks (DBNs) are used for large-scale real-world problems. In this article I extend ΦMDP to ΦDBN. The primary contribution is to derive a cost criterion that allows to automatically extract the most relevant features from the environment, leading to the “best” DBN representation. I discuss all building blocks required for a complete general learning algorithm, and compare the novel ΦDBN model to the prevalent POMDP approach.
Up- and Down-Projection Unit in a Dual Branch Scenario for Single Image Super Resolution

High-Resolution images are used in many fields, such as medicine, astronomy, security, and monitoring. However, in many cases, digital cameras usually do not have the desired resolution. Thus, the Super Resolution field has emerged in order to assist this need. The most used methods nowadays are based on only a single image super-resolution (SISR), and the Convolutional neural networks (CNN) are considered one of the potential methods for this task. In this paper, we analyze a dual branch model that uses the up- and down-projection units proposed by [1], and it was possible to verify that the dual branch model can outperform the single branch model. Using more up- and down-projection units can lead to a better performance in the dual branch scenario, and our model outperforms one of the proposed models of [2].
Student Management in Higher Vocational Colleges from the Perspective of People Oriented

Higher vocational colleges are an important part of China's education. They can do a good job in student management, effectively promote the cultivation of talents and improve the quality of talents. Although many higher vocational colleges continue to reform student management, there are still some problems in this process. The reform and development of higher vocational colleges is the key link to do a good job in student management, which is related to the quality of school work and the good image of school. This paper analyzes the current situation of student management in higher vocational colleges and discusses the necessity of implementing people-oriented management. From these two aspects, we will study the reform of student management in higher vocational colleges under the people-oriented environment.
Incorporating Multimodal Cues for Advertorial Discovery

Commercial advertorials shared on websites are usually designed to pretend as normal social news for commercial benefits. The analysis of the commercial intents embedded in advertorials can greatly help media platforms personalize content. However, commercial intents are not only concealed in news texts but also conveyed by news images explicitly or implicitly. Consequently, how to effectively extract and incorporate the crucial cues of multiple modalities has been emerging as an important but challenging problem. Motivated by this observation, we propose a framework Multimodal Advertorial Discovery Model (MADM) to estimate the commercial intents embedded in the multimodal social news. Specifically, a novel Cross-graph Fusion (CGF) strategy is developed to achieve a soft assignment to incorporate images and text and generate comprehensive multimodal representations. The extensive evaluations demonstrate the superiority of our proposed system in multimodal-based advertorial detection and analysis.
Research on Hypothesis Testing Method Based on Reliability Theory

This paper studies another method for the reliability test of existing structures. The distribution function value z(v,n) is found by MATLAB software programming. Hypothetical test is performed on variable coefficient v by using distribution z(v,n), and the it is applied to existing structural reliability evaluation, which explains that this method is another feasible method for the structural reliability test.
Quantitative and Visual Exploratory Data Analysis for Machine Intelligence

Exploratory data analysis is a technique to analyze data sets in order to summarize the main characteristics of them using quantitative and visual aspects. The chapter starts with the introduction of exploratory data analysis. It discusses the conventional view of it and describes the main limitations of it. It explores the features of quantitative and visual exploratory data analysis in detail. It deals with the statistical techniques relevant to EDA. It also emphasizes the main visual techniques to represent the data in an efficient way. R has extraordinary capabilities to deal with quantitative and visual aspects to summarize the main characteristics of the data set. The chapter provides the practical exposure of various plotting systems using R. Finally, the chapter deals with current research and future trends of the EDA.
A Novel Fast Algorithm for Mining Compact High Utility Itemsets

Mining High Utility Itemsets (HUIs) discovers itemsets making much profit in business from transaction databases. Therefore, mining HUIs is essential for Intelligence Business. To decrease the time for computing, several fast algorithms focus on discovering general HUIs which contain the maximum number of items. However, these studies still consume much time and memory because of processing all items in each transaction. Moreover, HUIs containing several items help decision makers to plan a business strategy more effectively. HUIs with a few items is not enough information. HUIs with many items require decision makers to consider the importance of each item. In this paper, we propose a compact list and a novel fast algorithm for mining compact HUIs to meet the need of decision maker and decrease both time and memory. The proposed list stores information of itemsets including items, utility, set of transaction identification, and type. Our algorithm including three main steps. First, a vertical segmentation is performed on the current database to form sub-partitions. Next, initial HUIs are mined and itemsets is listed from each sub-partition. Then, a bigger HUI is formed based on joining a HUI of a sub-partition to a HUI or an itemset of other sub-partitions in a same set of transaction identification. This approach obtains three robust advantages including without generating candidate, without re-scanning database to computing the utility of bigger HUIs, without re-scanning database when changing minutil. Experiments are conducted on benchmark dense databases. Results show that the proposed algorithm is better than state-of-the-art algorithms.
Does Impact of Using an Online Learning on Salesmanship Course in The Era Covid 19 Pandemic?

This research aims to explore the application of learning salesmanship courses during the COVID-19 pandemic, where there are social policies and physical distance conducted by online lectures, while salesmanship courses will be effective if done with many direct practices. Approved implementation of planning, implementation, and evaluation of the implementation of online learning with the support of learning applications such as learning modules and videos. This research was conducted using descriptive qualitative, discussion, and collect data about responses and submissions of learning applications consisting of modules and video sales used during the online learning process. The technique of collecting data is done by collecting, observing, and questioning. Data were analyzed qualitatively using NVIVO software. The results showed that learning tools in the form of salesmanship modules and videos received very high responses, and were effective in fostering student interest in learning, the results of data analysis showed that with modules and videos made learning salesmanship easier, especially on the topic of effective selling techniques online at a pandemic period, very relevant to current needs.
Simulation of Algorithms and Techniques for Green Cloud Computing Using CloudAnalyst

In our fast growing world, cloud computing took place and became a milestone in the new technology revolution. Cloud computing offers online services, wherever and whenever the user requests. The most leading companies such as Microsoft, Google, and Amazon have contributions to the development of this field and they offer their services to users, companies, and organizations by cloud computing. Because of the importance of cloud computing, maintaining the service sustainable and reliable is very important. This leads to high electrical power consumption and big emission of CO2. As a result, the concept of green cloud computing (GCC) has appeared and took the interest of service providers, researchers, and endusers. On the other hand, many algorithms have been developed to reduce power consumption and carbon emissions. In our paper, we simulate the efficiency of datacenters that use different algorithms for could computing (CC). In simulation, seven datacenters in different regions are used. There are seven users’ databases and these users make ten requests per hour, during a period of one year. During the process of simulation, we made tables of comparisons of the performance of algorithms to find out the best one to be used for GCC. Results show that the best algorithm for the GCC concept is the combination of the Round Robin Load Balancer Policy and ORT Broker Policy because it takes the least time response starting from sending requests until receiving the response.
Bio-Inspired Routing Protocol to Enhance Performance of Wireless Sensor Network

Abstract Tremendous advancements are seen in communication technologies for the past few years. Mechanisms and concepts used for transmission are facing continuous updates. The challenges arise when data pass from wired medium to wireless medium. The advent of WSN provides a new dimension in network research particularly towards controlled devices. In those types of networks, communication between sensor nodes entirely depends on sensing the location and tracking the changes that get happen. These kinds of sensor nodes permit WSN to (i) perform the operation in a broad range, (ii) receive the accurate information. Routing plays a major role in WSN to deliver the sensed information to the destination node. Poor routing ends with network failure. In this paper, bio-inspired optimization based routing is proposed to enhance the performance of the routing by minimizing the delay and energy consumption. Proposed protocol is inspired from characteristics and behavior of saddle goatfish. Results indicate that the proposed routing protocol attains the better performance than other routing protocols.