Miracle-dz/UniFAR-FTUs huggingface dataset:
Natural Language Processing with Improved Deep Learning Neural Networks

As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.
An automatic Smart Phone with IoT based Accident detection and alerting System

Manufacturing and sales of vehicles these days has increased due to its high demand. Due to increase in usage of vehicles, an alarming situation has been created for road accidents. Percentage of road accidents is getting increased year to year. Fatality rate due to accidents is also becoming higher. The reason behind the fatality rate with road accidents is not only the precautions taken while driving the vehicle but also the delay in emergency services. Many lives can be saved if the injured person is given a timely treatment. There are many reasons for the delay in timely treatment of injured persons. The reasons are delay in communication to the nearby hospitals and also the heavy traffic which can be the reason for the delay in ambulance service to reach the injured. A smart phone[1] based automatic detection of accident and notification system with IOT (Internet of Things) is proposed which can help in not only detection of accident but also a notification is sent to the nearby hospitals using a smart phone. Here external pressure sensor is integrated to detect the external force on the vehicle body. In this paper a measurement is done with the parameters like speed, accelerometer and change in angle with GPS.
Comparison of Variant Principal Component Analysis Using New RNN-based Framework for Stock Prediction

Due to the high complexity and dimensionality of stock price data, high volatility and uncertainty cause an over-fitting problem in predicting stock price. In this paper, we compare different combinations of variant Principal Component Analysis (PCA) methods and Recurrent Neural Network (RNN) based frameworks that can be applied to extract high-level features from a rich set of initial variables for producing 2-year ahead forecasts of the daily stock price of IBM and Wells Fargo. The objective of our comparative study is to find which combination performs best in forecasting different stock price data. The experimental results show that the best combination for IBM is 2-Directional 2-Dimensional PCA (2d2d-PCA) with Long Short-Term Memory (LSTM), which achieves accuracy of 91.77% based on R-Square (R2) and for Wells Fargo is 2d2d-PCA with Gated Recurrent Unit (GRU), which achieves accuracy of 92.47%. Our comparative study indicates that the best combination of dimension reduction method and deep learning model is unfixed for predicting different stock price data. In addition, it is confirmed that GRU is an efficient model in predicting stock price; and 2d2d-PCA performs as well as PCA with respect to other dimension reduction methods with less error.
Multi-Task CNN model for emotion recognition from EEG Brain maps

Emotion identification plays a vital role in human interactions. For this purpose, Computer-vision methods for automatic emotion recognition is nowadays a widely studied topic. One of the most studied approaches for automatic emotion recognition is processing multi-channel Electroencephalogram signals (EEG). This paper presents a new model for emotion recognition using brain maps as input and providing emotion states in terms of arousal and valence as output. Brain maps are a spatial representation of features extracted from EEG signals. The proposed model, called Multi-Task Convolutional Neural Network (MT-CNN), is fed with stacked brain maps of four different waves of different frequency bands: alpha, beta, gamma and theta, using differential entropy and power spectra density and considering observation windows of 0.5s. This model is trained and tested on the DEAP dataset, a well-known dataset for comparison purposes. This work shows that the MT-CNN nerforms better than other methods.
Fast and Accurate Machine Learning Compact Models for Interconnect Parasitic Capacitances Considering Systematic Process Variations

A novel modeling methodology is developed for interconnect parasitic capacitances in rule-based extraction tools. Traditional rule-based extraction tools rely on pattern matching operations to match every interconnect structure with corresponding pre-characterized capacitance formulas. Such a method suffers from three main problems including limited pattern coverages, potential pattern mismatches, and limited handling of systematic process variations. These problems prohibit rule-based methods from coping with the new capacitance extraction accuracy requirements in advanced process nodes. The proposed methodology overcomes these problems by providing machine learning compact models for interconnect parasitic capacitances that cover varieties of realistic cross-section metal patterns. Those models efficiently include the impact of systematic process variations on parasitic capacitances. Moreover, each model can handle thousands of patterns replacing thousands of existing capacitance formulas. The input to the models is a cross-section pattern that is represented by a novel vertex-based pattern representation. The models are implemented using two different machine learning methods: neural networks and support vector regressions. The two methods are tested and compared to each other. The proposed methodology is tested over thirteen test chips of 28nm, 14nm, and 7nm process nodes with more than 6.7M interconnect patterns. The results show that the proposed methodology provided outstanding accuracy as compared to field-solvers and rule-based models with an average error < 0.15% and a standard deviation < 3.3%, whereas the average errors and standard deviations of rule-based models exceed 6%, for the same test chips. Also, the computational runtimes of the compact models are almost 2.5X faster than rule-based models.
DNN-Based Speech Separation with Joint Improved Distortion Constraints

Considering that in different signal-to-noise ratio (SNR) regions, the amplification distortion caused by the overestimation of the gain function will have varying degrees of impact on the speech intelligibility, this paper proposes a deep neural network (DNN) speech separation with joint improved distortion constraints to further improve speech intelligibility. The algorithm first uses the segmented SNR to correct the overestimated gain function, and then combines the constraints of amplification distortion to further constrain the output amplitude of the estimated speech to obtain the final separated speech. Finally, the comparative experiments show that the proposed algorithm can significantly improve the speech quality and intelligibility of the separated speech.
SMF Multi-Point Synchronous Triggering System for Fast Transient Measurement of 500 kV Pumped Storage Power Plant

A SMF (single-mode optical fiber) multi-point synchronous triggering system is proposed in this paper. This system uses the partial discharge sensors embedded in basin insulators mounted on the GIS cavity to obtain a stable trigger signal. Triggering system transmits the optical trigger signal through the single-mode optical fiber. Therefore, this system is able to transmit the trigger signal using the communication optical fiber cable installed in the power plants without the need to lay fibers manually. Through theoretical and experimental analysis, the inherent time difference between each triggering channel is determined. The fast transient overvoltage generated by the breaker operation in 500 kV GIS at a pumped storage power station is measured by this system. The analysis of the measurement results shows that the system can reliably trigger the oscilloscope to record the fast transient waveform. The propagation time of the fast transient overvoltage wave between two measurement points obtained by the synchronous trigger device is basically consistent with the theoretical analysis.
Design Semantic Typicality Calculation in Conceptual Spaces

The assessment of typicality is a vital aspect of design evaluations. Based on the theories of fuzzy semantic cell and feature prototype model (FPM), we proposed an objective method to measure design semantic typicality. Design semantic typicality was assessed based on the Euclidian distances between a design case and its prototype. It could be calculated by the mean distance between the prototypes of design features and the prototype of the benchmark category in conceptual spaces. We validate this method in two experiments. Based on the analysis of empirical and experimental results, we confirmed that the proposed method is a promising approach to quantify design semantic typicality, which is able to largely reflect the human perception of design typicality.
Computing cluster and artificial intelligence in the analysis and application of college students' physical exercise behavior

Computing cluster and artificial intelligence in the analysis and application of college students' physical exercise behavior is studied in this paper. The algorithm is proposed under different scale job sets, the load situation of each node in the entire cluster is relatively balanced, and there is no vacant server situation. By using two algorithms to schedule the variance statistics of the load conditions of each node in the cluster under the same job scale, compare the load balancing performance of the two scheduling algorithms. With the theoretical support, the AI integrated smart training model is constructed, and applied to the physical exercise. The performance is tested under various data sets and the results are recorded to be efficient.
A Two-stage Deep Neural Network for Macro- and Micro-Expression Spotting from Long-term Videos

Facial macro- and micro-expression spotting is an important task in the micro-expression analysis. This paper presents a Two-Stage Macro- and Micro-expression Spotting Network (TSMSNet) to locate the temporal positions of macro-and micro-expression in long-term videos. It is composed of two sub-networks. The first sub-network is a Triplet-Stream Attention Network (TSANet), which uses three spatial feature extraction branches and attention mechanism to extract the spatial-temporal features. The TSANet is utilized to spot the macro- and micro-expression apex frames. According to the predicted apex frames, the initial expression intervals are recommended. The second network is a Spatial-Temporal Classification Network (STCNet), which utilizes the initial expression intervals to predict the multi-scale expression in-terval proposals. Comparative results show that the proposed expression spotting method has achieved the state-of-the-art performance in two benchmark databases CAS(ME)2 and SAMM Long Videos.
Technical Comparison of PWM and MPPT Charge Regulators

In recent years, many small and large-scale solar power plants have been installed worldwide. Off-grid small-scale prosumers and solar power plants also use energy storage systems for more efficient energy harvesting. Different charge control management systems extend the life of expensive batteries and improve the benefits in solar power plants smaller than 5 kW. This study compares the performances of charge control systems with PWM and MPPT algorithms under constant and variable solar radiation Radiation values measured in YTU Davutpaşa campus were used in the scenarios. Obtained results are given in detail.
Epileptic Seizure Detection based on LSTM Model using Noisy EEG Signals

In the field of automation for accurate diagnosis of Epilepsy (neurological disorder) computer vision and deep learning are linchpin. Epilepsy is diagnosed with the help of Electroencephalogram (EEG) recordings by neurologist or field specialist. EEG interprets recordings in the form of signals. It provides pathological as well as physiological information which is far better than other methods in terms of time taken and technical artifact used for recognizing neurological disorder i.e. epilepsy. EEG signals represents in form of brain waves which shows electrical activity of brain and condition of brain as well. In neuroinformatics, mechanized EEG seizure detection has pre-eminent significance for researchers. Seizures predicted before occurrence definitely results to patient safety with respects to life quality. In this paper LSTM model is used for analysis of EEG signals by adding Gaussian noise on Bonn EEG dataset. Adequacy of the approach is determined on various metrics i.e. accuracy, sensitivity and specificity. The proposed technique has achieved 100% accuracy, 100% sensitivity and 100% specificity only in 30 epochs. The desired results signify that detection approach for seizure possess future for medical convention too.
A Comparative Study on Inversion Algorithms of Aerosol Extinction Coefficient Based on BP and Elman Neural Network

Lidar, as an important technical means for detecting atmospheric aerosols, has been widely used in fields such as atmospheric radiation and light propagation. The aerosol extinction coefficient, as a physical parameter for quantitatively analyzing the light scattering characteristics of atmospheric aerosols, was of great significance for further research on the characteristics of atmospheric aerosols. However, the current use of traditional methods to retrieve aerosol extinction coefficient with disadvantages such as many assumptions, complicated calculations, and uncertain measurement results. In the paper, the neural network model, which was widely favored by the majority of scientific and technological workers, was used to invert the aerosol extinction coefficient. After extensive training using the BP and Elman network, it was found that the network could directly predict the extinction coefficient of atmospheric aerosols by detecting the returned lidar signals, which effectively improves the retrieval efficiency of atmospheric aerosols. The experimental results showed that after comparing the two networks used, it was found that Elman has a better effect than BP in retrieving atmospheric aerosol extinction coefficient, and the accuracy advantage was more obvious. Therefore, the practical value and application prospect of using Elman neural network were better when inverting aerosol extinction coefficient.
Emotional Design for Intelligent Products Using Artificial Intelligence Technology

The rapid development of artificial intelligence (AI) technology makes products more intelligent, and users’ demand for products also shows a stronger emotional experience. The relationship between the emotional design methodologies for intelligent products is discussed. By using some AI-based algorithms, the multi-step design method consists of intelligent demand analysis, subitem decomposing, design process with relationship matrix, and quality evaluation is proposed. Some real-world cases show that emotion design for intelligent products based on AI technology not only improves the efficiency of design but also brings users a better emotional experience.
Temporal Graph Representation Learning for Detecting Anomalies in E-payment Systems

In this paper, we consider the problem of detecting anomalies in real-world E-payment systems. The E-payment systems can be viewed as a temporal interaction graph, with nodes representing users and edges representing multidimensional transaction sequences between them. To identify illicit users in the E-payment network, we formulate it as a node classification task and apply recent advanced temporal graph representation learning approaches to solve the problem. To fully capture and model the dynamics of the E-payment network, we propose the model, Graph Temporal Edge Aggregation (GTEA), a framework of representation learning for temporal interaction graphs. Instead of aggregating all related interactions of a node over time, GTEA learns to model temporal dynamics for each edge in the graph. Such characteristics enable it to capture pairwise interactional patterns, which can be discriminative for modeling relationships. In addition, a sparsity-inducing self-attention mechanism is incorporated into the framework, which highlights important neighbors and simultaneously filters out a large number of long-tail noises. By combining interactive temporal dynamics with multi-dimensional relational dependencies in a network, GTEA can learn fine-grained representations for entities in an E-payment network, which can be used to distinguish different roles of users for anomaly detections. We demonstrate the effectiveness of GTEA over existing state-of-the-art models with three real-world E-payment datasets on node classification tasks.
A Survey on Balanced Binary Search Trees methods

Binary Search Trees (BSTs) are fundamental data structures in computer science; because of their implicit key sorting and linked node structure, they provide effective sorting and simple update operations. They achieve their highest performance when they are balanced. A non-balanced Binary Search Tree is no more efficient than a regular linked list. In the present literature, multiple methods to balance Binary Search Tree were proposed. Previous researches have focused on solving the question “What is the best Balanced Binary Search Tree method adapted for data management scenario? ”, but recently proposed methods have not been studied yet. In this paper, we have conducted a comparison of classical and most recently balanced Binary Search Trees methods, to allow software developers to choose the most efficient Binary Search Tree method based on the data management problem they face.
Multi-Class Classification of Different Region Pop Songs using Spotify Database

Music Information Retrieval is a field that is interested in extracting information from musical compositions whether it is songs, instrumental pieces or scores of various genres. With the development of Machine Learning (ML) and Artificial Intelligence (AI) techniques, better models are used to infer songs and scores. These inferences are later used in detecting copyright breaches, categorizing songs as per user, developing music recommending applications and many more. Though our understanding of music has increased tremendously, linguistic based research has been limited. The work in this paper focuses on analyzing data-sets of songs of different Indian linguistic communities on the basis of some predefined musical parameters like speechiness, loudness, acousticness etc. The only similarity is the songs in comparison of different languages have the same mood or valence (positiveness). This work will help to understand how different linguistic music having the same mood, has different musical features. The method used for this project is Multi Class Neural Network for training and thus testing the data-set. The data-set is collected from a popular music app using the inbuilt Application Programming Interface (API) of the app.
A Gysel Power Combiner with 20W Wideband Isolation Termination and 25% Fractional Bandwidth

In this paper, a Gysel power divider with 2.5 GHz center frequency and 40% fractional bandwidth was designed for use in high-frequency high-power circuits. Rogers RT5870 with dielectric constant of 2.33, dielectric thickness of 0.787 mm, copper thickness of 35 µm and loss tangent of 0.0005 was used as the dielectric substrate. Vishay FT10302N0050J termination was used as isolation resistor in the study. According to the simulation results, the Gysel power combiner designed has a maximum insertion loss of 0.1 dB, amplitude imbalance of 0.05 dB and phase unbalance of up to 0.2 degree over the 2.2-2.8 GHz band. Since the layouts of the microstrip lines were created by paying attention to the high frequency design principles, meaningful difference was not observed between the circuit and electromagnetic simulation results. When the actual circuit is measured, a insertion loss of up to 0.2 dB over the 2.2-2.8 GHz band, an amplitude imbalance of up to 0.1 dB between the input ports and maximum 1 degree phase imbalance results were obtained. Since isolation resistor has an impedance of about 50 Ohm and a power resistance of 20W throughout the DC-18 GHz band, the implemented power combiner can be used to obtain higher output powers by combining 20 Watt and above power amplifier circuits.
Analysis of optical MPPM channel capacity without background noise

Based on the channel model of Multi-pulse Position Modulation (MPPM) without background noise, the expression of channel capacity, the expression of information transmission rate and the expression of energy efficiency of MPPM with protective time slot were derived according to information theory. Channel capacity, information transmission rate and energy efficiency of MPPM and PPM were analyzed. The simulation results show that compared with PPM, MPPM offers better channel capacity, information transmission rate and energy efficiency when parameters are selected properly such as optical pulse power, the reciprocal of system bandwidth and the average number of photon for single pulse.
A Prediction Model of Domestic Animated Films and Audience Psychology Based on Facial Expression Recognition

Nowadays, the wave of new media has swept across all walks of life, penetrated into every aspect of people's daily life, and has had a huge impact on domestic animated films. The production and dissemination of domestic animated films are changing to focus on the needs of the audience. Based on the facial expression recognition algorithm of deep learning, this paper establishes a model to predict the audience psychology of domestic animated films. This article uses the three levels of “audience sensory needs”, “audience aesthetic needs”, and “development status and countermeasures” of animated films in the new media environment as the research basis, combined with deep learning algorithms, to make accurate estimates of audience psychology.