Reviewerly/exHarmony huggingface dataset:
A Systematic Review of Machine Learning Approaches for Classifying Indian Sign Language Gestures and Facial Expressions

Indian Sign Language is the primary mode of communication known to persons who use Indian Sign Language for people who have hearing or language deficits. Different types of machine learning models are used to broaden the scope of communication for those with impairments and illiteracy. There are numerous machine learning models for analyzing gestures, postures, and facial recognition in Indian Sign Language for single-handed and double-handed signals. The present study on hand gestures, recognition, and translation intends to build an essential foundation for developing a platform to facilitate communication for the s pecially-abled with anyone. Machine learning algorithms generally focus on letter recognition or a few fundamental indicators. Communication is essential for exchanging ideas, thoughts, and feelings. Sign language is a kind of communication that uses hand motions. This is aimed toward those with impairments such as muteness and deafness. Machine learning, a branch of artificial intelligence, will aid in identifying various hand motions and predicting the language created by those inputs based on those inputs[2]. Sign language has a grammar that is unique from and independent of English. When compared to English, SL allows for far more freedom in word order. Tense is marked morphologically on verbs in English, but SL (like many other languages, such as Indian Sign Language) communicates tense lexically using temporal adverbs. The structure of ISL and English differs at the phonological level as well. Signed languages, like spoken languages, include a degree of sublexical structure that includes segments and combinatorial rules; however, phonological elements are manual rather than vocal. The way spatial information is conveyed in English and ISL differs substantially. All Deaf people are illiterate in written English. As an output, the SL text can be produced. SL is just physically executed English, where English and SL share the identical linguistic structure-that one is a straight encoding of the other. Many software designers mistakenly believe that deaf users can always access printed the English language in a user interface. Many designers feel that if auditory information is also supplied as written English, the deaf user's demands will be addressed. Prepositions such as “in,” “on,” and “under” are used to indicate locative information in English, as in many other spoken languages. On the other hand, SL encodes locative and motion information via verbal classifier formulations in which hand shape morphemes define item type, and the location of the hands in signing space schematically depicts the spatial relationship between two things. Thus, English and ASL differ significantly in phonological, morphological, and syntactic areas.
Data Classification Based on Broad Learning System with Hybrid Genes-PSO

Broad Learning Systems (BLS) have recently been shown to be effective and efficient. This paper reviews the basic structure of BLS and proposes a BLS based on improved PSO. The proposed model optimizes the parameter <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> in the ridge regression method used when the BLS directly computes the pseudo-inverse to obtain the network weights. In many problems, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> is usually set as close to zero as possible, but in practice, different <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> values are used, and the accuracy of the model will be different. If a method can be found, and it is different from the tedious and complex grid analysis, it can find the value of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\lambda$</tex> that makes the model as accurate as possible while taking into account the accuracy and efficiency, then the overall performance of the network can be improved. Finally, experimental results on multiple fraud and cancer datasets demonstrate, collected from the UCI repository, that this method is better than selected state-of-the-art methods, such as stacked autoencoders, deep belief networks, etc., in terms of accuracy and training speed. And the results of this paper also show the conclusion that the method of parameter selection can greatly decrease the training time of the traditional BLS on the classification scene on the UCI dataset.
ANN Based Prediction of Module Temperature in a Single Axis PV System

Photovoltaic technology is one of the most effective and cleanest methods of obtaining energy from the sun. The efficiency of modules that is one of the most basic components of photovoltaic systems, is very sensitive to environmental variables. The air temperature and the electric current that passing through the panels cause the panels to heat up. Temperature negatively affects the efficiency of the panels. In photovoltaic systems, the panel temperature must be determined in order to know the rate of heat losses, which is one of the many types of losses. In this study, an artificial neural network model was developed that determines the temperature of a single axis tracking solar panel according to environmental variables. For the development of the model, 36699 data rows were used, which were measured and recorded with a data logger for one year. In this model, the module temperature is the dependent variable while the solar irradiance, ambient temperature, wind speed, relative humidity and panel power are selected as the independent variables. In the developed model, there is a 98.87% correlation between the actual values and the estimated values. The developed model predicts the module temperature very well according to the actual values with 1.45 MAE, 4.27 MAE, 6.37% MAPE and 2.24% RSE performance criteria. By knowing the module temperature, the amount of heat losses that will occur in photovoltaic systems can be calculated. In addition, estimating the panel temperature value can be used as an important parameter in the organization of cooling processes to increase efficiency.
Determining Energy Consumption in Heterogenous Cloud Computing by Usage of RMRECFS Workflow with Cost Confinement

For large-scale workflow applications, the cloud is a promising platform. The pay-per-use model is used. In cloud data centers, reducing the energy utilization of priority-limited cost-cutting workflows limits became a hot topic. The majority of existing scheduling algorithms focus on the completion time as well as cost of a particular workflow application while working within cost limitations; These algorithms, however, fail to account for energy savings. The critical task remapping and frequency scaling RMRECFS (Re-Mapping based Reducing Energy Consumption with Frequency Scaling) method is used in this work to provide an approach for reducing energy consumption. Energy utilization reduction and key task remap are two steps of this technique. The cost budget, critical work path, and expandable budget factor are all included to determine the adjustable cost budget and additional costs in the first stage. To execute a preliminary task and task mapping virtual machines when adhering to configurable budget restrictions, most workflow tasks are then distributed to VMs that spend some least amount of power. By reallocating key jobs and applying frequency scaling to virtual machines based on spare costs, the second phase minimizes power consumption owing to task relocation. Experiment with two different types of workflow applications at various ranges show that the provided RMRECFS algorithm efficiently saves energy usage while staying within budget limitations when compared to the conventional RMREC algorithm
Realtime and Secure Online Event Voting Management System

In this project, we are going to actualize a web-based application that enables the user(s) of a particular organization to pitch their corresponding votes. This is achieved by the regulation of JSP Servlets in it. As a backend, we have used the “MySql” for the database connectivity. In order that the code is easily understandable for future modifiers, we have used the Separation of Concerns by the deployment of the MVC (Model View and Controller) pattern for achieving the desired tasks. This project enables the user to register oneself using the right credentials and after that, the user is logged in after his/her details get stored in the database. Then the user can visit his/her dashboards to cast their respective votes and to view their profile on the website. The count of votes a user can cast is only one. This is possible within an organization. The user-side and administrator-side validations are done here. We use JSP/Servlets to process the activities from the server side. In this specific project, our plan is to implement an online event legal system with options just like the schemes that the particular party has enforced, supporting the options we tend to square measure progressing to vote.
A Novel Image Processing System to Recognize and Classify Images using Integrated Advanced CNN and FPGA for IoT based Applications

FPGAs are ideal for deep convolutional neural network (CNN) acceleration because of their high parallelism and energy economy. Automatic compilers for the generation of network-specific FPGA accelerators have been developed in recent years. The use of network-specific accelerators, in combination with more cascade deep CNN algorithms, necessitates running-time re-configuration of FPGA devices. Edge devices may have a hard time with this kind of reconfiguration. FPGAs are a potential option for embedded applications. There are several uses for the Zynq-7000 plus FPGA, including a dual-core ARM Cortex-A9 processing system with double wrap up. The problem statement prompted the development of a hybrid model combination of ACNN and FPGA. FPGA provides rich analogous computing resources with high power competence, making it ideal for deep convolutional neural network (CNN) acceleration. Employing CNN with FPGA is a stimulating work. FPGA executions ensure memory restrictions and fall short to encounter source necessities for critical tasks. Proposed system yields good detection rate i.e 0.08 and improved frame processing rate as 124.82. Here detection time also observed to be reduced as 9sec. furthermore proposed one outperforms to improve efficiency levels.
Unsupervised learning statistical downscaling based on residual network of attention mechanism

Because the numerical model dynamic downscaling takes a long time and requires high computing resources, a statistical downscaling model of residual network based on unsupervised learning is proposed. First of all, this network is inspired by the “zero-shot” super-resolution, using ResNet50 as the network residual module, making the network deeper while simplifying the degree of training. It link the low-scoring information of low-scoring pictures to the depths of the network to improve the effect of the network on feature extraction. In addition, KernelGAN is used to train it with the low spatial resolution pictures output by the WRF mode instead of the down sampling blur kernel of the low resolution pictures corresponding to the input in ZSSR. Finally, the channel attention and the spatial attention mechanism are added to change the equality of feature extraction by the main body network. The simulated high temperature image (T2) of Tianjin Port and Bohai Bay two meters from the ground, which is better than Bicubic, Kriging, SRCNN, ZSSSR, KernelGAN+ZSSR and other methods.
Computer Intelligent Investment Strategy Based on Deep Reinforcement Learning and Multi-Layer LSTM Network

Investors frequently make business about volatile assets with the goal of maximizing their profits. This aim of this article is to establishes a model to study the investment strategy of bitcoin and gold. It's not always accurate to use algorithm based on traditional time series model to predict the prices. Therefore, The multi-layer LSTM weighted model of multi-layer LSTM network this article puts forward is used to study the input time span to factor the influence of output time span. By using the embed approach, this model can achieve higher accuracy compared with LSTM model by influencing the stacking sequence generation cycle and training model. Meanwhile, this model introduced the Attention mechanism and can make short-term and long-term forecasts continuously. It also quantified the fluctuation of MACD, RSI, Apriori and other parameters to construct the risk measurement index. The higher the periodic price fluctuation is, the greater the risk can be. The results show that timely business maximizes profits. An initial $1,000 investment on September 10, 2021 can even profit more than $900,000. In order to prove the model provides the best strategy, depth of reinforcement learning model is set up. Bitcoin and gold history feature information of state data matrix and agent within the depth of neural network is utilized to extract the stable characteristics of data expression. Based on these features, this model utilized optimized strategy method of three reinforcement learning algorithms. Combined with the current reward, it optimized the relevant parameters of the deep neural network to obtain the optimal investment strategy in exploratory ways. Moreover, the model will be ran twice to test the sensitivity under different transaction costs (investment decision results) by setting points of 0.5%, 1%, 1.5% and 2% according to the percentage of increase and decrease of transaction costs. The results show that the model has good robustness, which means the model constructed in this paper has good results under different transaction costs.
A Feature Guided Denoising Network For Adversarial Defense

As neural networks are playing a more and more significant role in many fields, they are also under the risk of being attacked by adversarial examples, which becomes a great challenge to the whole community. Due to this problem, networks cannot provide the guaranteed security when they are used in some security-sensitive scenarios. In this paper, a feature guided denoising network for adversarial defense is studied. Specifically, a denoising network is designed to remove possible adversarial perturbations on the input image. And a novel training method of the denoising network is proposed to improve the performance, in which deep features extracted from clean examples by the pretrained classifier is used as supervision information in the training process. Experimental results reveal that the proposed method shows satisfactory performance on defending against several white-box adversarial attacks. Besides, combination of the proposed method and adversarial training is studied, which achieves very good results compared to the other experiments reported in this paper.
Research on English Translation System Based on Computer Artificial Intelligence Technology

Phrase table is a core component of phrase-based statistical machine translation system. The phrase table extracted based on heuristic method is seriously affected by word alignment errors and misaligned words, and the extracted phrases are not syntactical. phrase. The system replaces the original word-based model with a translation model based on phrase template alignment, and proposes a method to calculate the translation probability of phrase templates. In this paper, the IMT method is improved from the aspects of interaction and decoding algorithm. In terms of interaction, the translator is allowed to select the correct translation for the source language phrase from the list of phrase translations before translation. This paper also proposes a diversity sorting algorithm based on phrase table to improve the diversity of phrase candidates. During decoding, words with a reproduction degree of 0 and a high frequency of occurrence are introduced to make the translation result more reasonable. The experimental results show that the phrase-based statistical machine translation method can quickly and effectively build a Chinese-English machine translation platform.
Research on Diversity Composition Coefficients and Influencing Factors Based on High Bit Rate FM Baseband

Based on the 4203 high code rate FM band, the actual equipment performance of the composite synthesis technology of the improved signal mass is studied, and the influence factors of the two key analysis diversity synthesis systems are studied, and the relationship between the synthesis agc and the synthesis system is studied.
Experimental study of reverse reconstruction for transformer radiation noise based on ARMA model

Based on the acoustic radiation theory and laser vibration measurement method, the surface vibration and radiation noise sound pressure of the dry-type transformer model are measured experimentally, and the frequency response function method and surface vibration velocity method are used to obtain the radiation exponent of the transformer, respectively. On this basis, the autoregressive moving average model (ARMA) is used to study the reverse reconstruction of transformer noise, and the predicted values of sound pressure and sound pressure level are obtained and compared with the experimental values. The results show that the radiation exponent obtained by the frequency response function method is slightly smaller than that of the surface vibration method by about 1~1.5 dB. The difference between the sound pressure obtained by the noise prediction algorithm based on the ARMA model and the experimental test sound pressure is about 0.015 Pa, and the difference in sound pressure level is about 1 dB. The prediction algorithm based on ARMA model and laser vibration measurement for reverse reconstruction of noise has good feasibility and can be applied to prediction of radiated noise of other structures.
Prototyping Agents for Resolving Opinion Biases Toward Facilitating Sublation of Conflict in Web-based Discussions

The term “sublation” (or “aufheben”) refers to the process of arriving at an agreed upon answer to two opposing arguments without denying either of them. In this study, we conducted a discussion experiment in which we quantified the degree of sublation and analyzed the results to determine the factors that contribute to the cessation of conflicting opinions in discussions. Our findings revealed a weak positive correlation between the number of URLs posted as evidence for one's opinion and the degree of sublation of the consensus proposal. In actual discussions and debates, however, there are times when everyone makes the same argument, with little or no opposing views, resulting in biased opinions. To address this problem, we developed a method to eliminate bias in opinions, in which an agent posts information that reinforces the opinion of a minority in a discussion. The experimental results demonstrate that GPT-3, a natural language processing model, can be applied to summarization of relevant information for information provision and the resolution of opinion bias.
Real-time Measurement of Vibration Frequency of REPDS Based on Vision

Aiming at the vibration frequency measurement of REPDS (Reduced Pressure Drop Stick), a measurement method based on vision is proposed. Firstly, the parameters of ellipse features of top disk and top disk mark in each image of REPDS are extracted. Next, these parameters are used to measure the position and attitude of the corresponding space circle. Then, the normal vector of the REPDS at a current time is calculated by using the measured coordinates of the center of the space circle. And the attitude angle of the REPDS is calculated by the normal vector. Finally, the vibration frequency can be obtained by Fourier transform and spectrum analysis of the attitude angle sequence obtained from multiple image processing. It is proved that the method is feasible by using simulation image data. The measurement error is less than 3.3%. The method is optimized with global fixed threshold and image subsampling. Real-time measurement of vibration frequency based on the method is realized on RK3588 embedded platform, and the measurement frequency is greater than or equal to 1Hz.
Intelligent mask recognition and voice dialogue system based on Raspberry Pi control

As New Coronavirus continues to mutate, virulence and infectivity are constantly strengthened. 2019-nCoV has become the most deadly virus in recent years. Its main transmission route is droplet transmission. Wearing masks can effectively block the spread of viruses. Mask is an important defense line to prevent respiratory infectious diseases, which can reduce the risk of New Coronavirus infection. The mask can not only prevent the patient from spraying droplets and reduce the amount and speed of droplets.but also block the droplet core containing virus and prevent the wearer from inhaling. In public places.especially in places with large passenger flow, it is a drop in the bucket to rely solely on manual supervision of customers wearing masks. In order to solve this problem.an intelligent mask detection system is designed, It supports the corresponding models of all mainstream frameworks for face mask detection (pytorch, tensorflow, keras,mxnet and cafe) (the model trained by keras and other framework models converted),and provides the reasoning code of all five frameworks. At the same time.it also has the voice dialogue function,which can wake up the voice robot in real time and achieve man-machine communication. Let inspectors feel more at ease and consumers feel more at ease.
Analysis and Application of Power Equipment Production Quality Based on Data Hierarchical Processing

The Internet of things (IoT), cloud computing, big data technology, and artificial intelligence algorithms have developed rapidly. However, the data generated by the above process, due to its characteristics of multiple sources, large volume, rapid growth, heterogeneity, and complicated correlation, pose challenges to orthodox data processing methods. This research proposes a data layering model based on the weight of data and access frequency. Storing data with different functions and adjusting the data level according to various conditions enhances the query, reading efficiency, and data reliability of massive data. Furthermore, this research provided a solid foundation for subsequent data analysis. Specifically, we analyzed he processed data according to the technique benchmark of electrical devices and exhibited it graphically in the validation part.
DIRECT: Toward Dialogue-Based Reading Comprehension Tutoring

A major challenge in education is to provide students with a personalized learning experience. This study aims to address this by developing a dialogue-based intelligent tutoring system (ITS) that imitates human expert tutors. The ITS asks questions, assesses student answers, provides hints, and even chats to encourage student engagement. We constructed the Dialogue-based Reading Comprehension Tutoring (DIRECT) dataset to simulate real-world pedagogical scenarios with the assessment labels and key sentences to support tutoring. The DIRECT dataset is based on RACE, which is a large-scale English reading comprehension dataset. In addition, we propose a neural pipeline approach to model the tutoring tasks and conduct a comprehensive analysis on the results, including a human evaluation. The results show that our model performs well in generating questions, assessing answers, and chatting, showing high potential although some challenges remain. The proposed model provides a good basis for further development of dialogue-based ITSs.