Reviewerly/exHarmony huggingface dataset:
Text Feature Extraction and Representation of Chinese Mental Verbs Based on Deep Learning

There are some problems in feature extraction and representation of Chinese mental verbs, such as low accuracy and low efficiency. In order to further improve the computational efficiency and accuracy of Chinese mental verb text, based on deep learning theory, activation function and damage function were used to optimize the original model. Considering the calculation method of model gradient, the optimization model describing the characteristics of Chinese mental verbs is finally obtained. The model can be used to analyze the variation of the characteristic parameters of Chinese verbs and the method of representation. Finally, the model error is analyzed by the method of comparative verification. Relevant studies show that the number of outputs and output results corresponding to softmax function will influence the test results of the model. By comparing the curves, it can be seen that the curve corresponding to the output number has an obvious increasing trend, while the corresponding output result curve has an opposite changing trend. The linear and nonlinear characteristics of the two curves are obvious. The real value of the mean square error function shows a change of linear increase, while the corresponding output value shows a change trend of gradual decline, which indicates that the two kinds of data have different influences on the model under related algorithms. It can be seen from the error data that the gradual increase of independent variables will improve the accuracy of the test results. Five different Chinese mental verb parameters have different manifestations in the deep learning model: among them, declarative verbs fluctuate in a small range and have little corresponding influence. However, the fluctuation of nondeclarative verbs and positive and negative declarative verbs is relatively small, and the curve is relatively stable. Negative verbs have a positive influence on the test output. Double negative verbs have negative effects. Finally, the accuracy of the model is verified by calculating the difference between experimental data and model data. This research can provide theoretical support and model verification method for the application of deep learning model in other fields of Chinese language.
Detecting Cyberbullying with Text Classification Using 1DCNN and Glove Embeddings

In recent years, the number of social networking sites has exploded, providing a platform for individuals all over the world to connect and discuss their common interests. With the increased use of the Internet and social media platforms, it is no surprise that young people are utilizing these technologies to injure one another, and this happens on a variety of social media sites and apps. In a few of hours, a 10 s Snapchat post can cross platforms and go viral on Facebook, Instagram, and Twitter. The bulk of prior studies have used traditional machine learning models to tackle this challenge, and the bulk of the produced models in these researches are only adaptable to one social network at a time. Deep learning-based models have made their way into the identification of cyberbullying occurrences in recent studies, claiming to be able to overcome the limits of traditional models and increase detection performance. Given the negative effects of cyberbullying on victims, it is critical to identify effective ways to detect and prevent it. Deep learning can aid in the detection of bullies. The main goal is to identify the best algorithm for detecting cyberbullying using 1DCNN, Bidirectional Encoder Representations from Transformers (BERT), and Recurrent Neural Networks (RNNs) (RNN). In comparison with the other two models, 1DCNN and glove embeddings produce more exact results.
A Graph-Based Extractive Assamese Text Summarization

This paper proposes an approach to introduce a text summarization in the Assamese language as it has been observed that text summarization is an important natural language processing application developed to condense the original or main text by retrieving the relevant information while retaining the main matter of the text. In the proposed approach, we have presented our idea of text summarization with the help of a graph-based system where we have denoted the document as a graph and in which the vertices of the graph depict the sentences. For our objective, Assamese WordNet has been considered in the whole text summarization process and the said process includes three major phrases: Pre-processing phrase, features extraction and graph construction stage with finally applying LexRank and extraction of summary. We believe that this proposed approach yields better results when contrasted with the statistical approach and previous graph-based approaches in Assamese in terms of usage of nouns and amalgamation of semantic similarity and statistical features.
Epithermal Vanadium Mineralization in Sonia Sandstone of Marwar Supergroup, India

Present study of vanadium mineralization in the Sonia sandstone of Jodhpur Group shows first hydrothermal activity in the Sonia sandstone of Marwar Supergroup. The Sonia sandstone served as a host for the mineralizing solutions. The vanadium mineralization occurred in sandstone- gritty sandstone-conglomerate along faults. Field relationship and petrological study suggests that mineralization is filled by epithermal solutions and provide a better understanding of the timing and nature of the alteration processes. Though the source of the vanadium is not well constrained, but is possibly from the partial melting of banded gneissic complex-II (Sirohi Group), the source (crustal) melt had assimilated material from the rocks of Sirohi Group in significant amount. Deposition of tuffs at top of Sonia Formation (Chhoti Khatu) and emplacement of last stage Malani dykes suggest that the last phase of Malani magmatism generated heat for the partial melting of banded gneissic complex-II (Sirohi Group). The occurrence of vanadium in sandstone—gritty sandstone—conglomerate from Sonia Formation of Marwar Supergroup has opened a new chapter to explore uranium which generally occurs after vanadium mineralization in conglomeratic sedimentation.
Processing of Multispectral ASTER Data to Delineate Bauxite Abundant Zones and its Geochemical Characterisation Deposited over Deccan Traps of Central India in Mainpat Plateau, Surguja District, Chhattisgarh

The current study deals with the application of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data to delineate bauxite deposits over Mainpat plateau, Surguja district, Chhattisgarh, India. Application of specific band combinations help to delineate bauxite in the (SWIR) region (2.16–2.33 µm) and visible near infrared (VNIR) band (0.4–1.0 µm) of multispectral ASTER data. Various image processing techniques like atmospheric correction, minimum noise fraction, pixel purity index, n-dimensional visualiser, and spectral angle mapper were applied to ASTER image to delineate bauxite rich zones. The pure pixels were differentiated from the mixed pixels using pixel purity index which were further subjected to n-dimensional visualiser to identify the target end-members. The presence of bauxite was confirmed by comparing the spectral signatures of end-members with in-built spectral plots of United States Geological Survey spectral libraries and categorized as bauxite and ferruginous bauxite. Finally, the end-members were categorised as bauxite and ferruginous bauxite and mapped using spectral angle mapper. The result obtained was further compared with the lithological map provided by Geological Survey of India for accuracy. Then XRF analysis was carried out for the collected samples to confirm the presence of bauxite and ferruginous bauxite. Geochemical analysis also categorized the samples as bauxite and ferruginous bauxite that added accuracy to the results obtained from remote sensing technique. This study might be considered as a time saving eco-friendly exploitation of natural resources and finally the potential of multispectral ASTER data for mapping mineral resources.
Transformer-Based Distillation Hash Learning for Image Retrieval

In recent years, Transformer has become a very popular architecture in deep learning and has also achieved the same state-of-the-art performance as convolutional neural networks on multiple image recognition baselines. Transformer can obtain global perceptual fields through a self-attention mechanism and can enhance the weights of unique discriminable features for image retrieval tasks to improve the retrieval quality. However, Transformer is computationally intensive and finds it difficult to satisfy real-time requirements when used for retrieval tasks. In this paper, we propose a Transformer-based image hash learning framework and compress the constructed framework to perform efficient image retrieval using knowledge distillation. By combining the self-attention mechanism of the Transformer model, the image hash code is enabled to be global and unique. At the same time, this advantage is instilled into the efficient lightweight model by knowledge distillation, thus reducing the computational complexity and having the advantage of an attention mechanism in the Transformer. The experimental results on the MIRFlickr-25K dataset and NUS-WIDE dataset show that our approach can effectively improve the accuracy and efficiency of image retrieval.
Optimized Negative Selection Algorithm for Image Classification in Multimodal Biometric System

Classification is a crucial stage in identification systems, most specifically in biometric identification systems.A weak and inaccurate classification system may produce false identity, which in turn impacts negatively on delicate decisions.Decision making in biometric systems is done at the classification stage.Due to the importance of this stage, many classifiers have been developed and modified by researchers.However, most of the existing classifiers are limited in accuracy due to false representation of image features, improper training of classifier models for newly emerging data (over-fitting or under-fitting problem) and lack of an efficient mode of generating model parameters (scalability problem).The Negative Selection Algorithm (NSA) is one of the major algorithms of the Artificial Immune System, inspired by the operation of the mammalian immune system for solving classification problems.However, it is still prone to the inability to consider the whole self-space during the detectors/features generation process.Hence, this work developed an Optimized Negative Selection Algorithm (ONSA) for image classification in biometric systems.The ONSA is characterized by the ability to consider whole feature spaces (feature selection balance), having good training capability and low scalability problems.The performance of the ONSA was compared with that of the standard NSA (SNSA), and it was discovered that the ONSA has greater recognition accuracy by producing 98.33% accuracy compared with that of the SNSA which is 96.33%.The ONSA produced TP and TN values of 146% and 149%, respectively, while the SNSA produced 143% and 146% for TP and TN, respectively.Also, the ONSA generated a lower FN and FP rate of 4.00% and 1.00%, respectively, compared to the SNSA, which generated FN and FP values of 7.00% and 4.00%, respectively.Therefore, it was discovered in this work that global feature selection improves recognition accuracy in biometric systems.The developed biometric system can be adapted by any organization that requires an ultra-secure identification system.
A New Hybrid TLPD Algorithm for Task Scheduling in Cloud Computing

In this paper we have designed the hybrid approach of combination of credit based task length &amp; priority algorithm and credit based deadline algorithm as well as compare the results with FCFS, SJF and task length &amp; priority scheduling algorithms. When we use the credit based task length &amp; priority scheduling algorithm to schedule the task without knowing the deadline of the task, it will cause the dead of the least deadline task. The deadline credit is also included so that assigning number of resources to the tasks in such a way that there will be maximum resource utilization and minimum processing time achieved. This paper presents the simulation results of the proposed methodology implemented with the help of Cloudsim and Net beansIDE8.0 and analysis of results.
A Centralized Accounting Method of Enterprise Accounting Cost Based on Fourier Transform Algorithm

With the rapid development of the economy, the challenges faced by enterprises are becoming more and more severe. In order to enhance the core competitiveness of enterprises themselves, enterprises pay more and more attention to accounting cost accounting. However, for a long time, enterprises have been using traditional accounting costing methods, which greatly hampered the development of enterprises. The Fourier transform (FT) algorithm is a commonly used information transformation method, which can simplify the complex cost accounting process. Based on this, this paper proposes a centralized cost accounting method for enterprise accounting based on FT algorithm, aiming to fundamentally change the traditional cost accounting thought. The article first analyzes and summarizes the general characteristics of cost accounting methods. Then, starting from the cost centralized accounting method, the paper optimizes the traditional cost accounting formula by using the FT algorithm and proposes a new accounting cost-centralized accounting formula based on the FT algorithm. Finally, starting from the reality and taking W company as the research object, the article focuses on analyzing the application of the centralized accounting method of accounting cost relying on the FT algorithm in the production and operation activities of the actual enterprise. After a series of experiments and analysis, we found that the centralized cost accounting method of enterprise accounting based on FT algorithm has a very high degree of fit with the actual situation, and the accuracy of cost accounting is as high as 70.01%. Moreover, in the process of enterprise cost analysis, the quality of accounting information of enterprises has been comprehensively improved by 31.9%. This fully shows that the centralized accounting method of enterprise accounting cost based on the FT algorithm can bring certain economic benefits to the enterprise and optimize the internal resource allocation of the enterprise.
Robot Trajectory Planning Based on the Energy Management Strategy

With the increasing demand for automated production technology in national defense, industry, agriculture, and other fields, the status and role of mobile robots are becoming more and more pivotal. The intelligent technology of robots is becoming more and more demanding in terms of reliability, stability, efficiency, and adaptability, and the autonomous navigation technology of mobile robots is facing new challenges. This paper introduces the basic principle of traditional A∗ algorithm, points out the problems of this algorithm such as cumbersome calculation, large turning angle, and unsmooth derived trajectory planning, and proposes an improved A∗ algorithm. The improved algorithm introduces a two-way alternating search mechanism to improve the efficiency of the search path, and the improvement of the heuristic function solves the problem that the two-way alternating search A∗ algorithm takes more time when there are obstacles perpendicular to the path on the way to the encounter. Simulation experiments in different raster environments prove that compared with the traditional A∗ algorithm, genetic algorithm, and simulated annealing algorithm, the improved A∗ algorithm can significantly improve the search path speed, while overcoming the disadvantages of many path turning angles and large turning angles and is an efficient and feasible algorithm for raster map environments.
Tool Paper: Time Series Anomaly Detection Platform for MATLAB Simulink

In the world of constant technological development, Cyber-Physical Systems have experienced considerable growth in complexity and diversity of application domains. Consequently, the complexity and subtlety of faults and cyberattacks have grown proportionally. Thus, traditional methods, statistical methods, and machine learning algorithms have gradually given way to new methods based on Deep Neural Networks (DNNs). However, existing researches tend to focus on specific datasets and deep learning models in an offline manner and lacks generalization. This paper presents a new platform implemented using MATLAB’s App Designer, Deep Learning Toolbox, and FIBlock. It brings together several advanced DL-based anomaly detection methods and allows the user to customize a wide range of parameters. The intended workflow comprises the following three steps: (i) Select or Upload Dataset, (ii) Configure DNN, (iii) Examine Detection Performance. In the first step, it gives users the freedom to choose between existing datasets from different Cyber-Physical System (CPS) domains or to upload their own datasets to perform studies. Several data pre-processing methods are available. We have tested our tool on three datasets. One is based on real-world scenarios and the other two are generated from Simulink models. In the second step, a DNN architecture can be selected, where Network hyperparameters and training options can be set via an easy-to-use interface. This helps to find the suitable values for each scenario. In the third step, results are displayed graphically by the tool, detailing and comparing different detection methods. For detection of static datasets (offline detection), the performance is reported to users using various statistical metrics, including precision, recall, and F1 score. If the results are satisfying, users can generate corresponding detection tools as blocks that can be connected to the system for simulation in real time (online detection).
Generative Modeling of Music

Generative machine learning (ML) models are a class of models that allow you to create new data by modeling the data generating distribution. For example, a generative model trained on images of human faces would learn what features constitute a realistic human face and how to combine them to generate novel human face images. For a fun demonstration of the power of ML-based human face generation, check out [44].
Completing Sentences

Completing sentences is a useful feature for text entry systems. Given a set of possible sentences, we want the system to predict a missing part of the sentence. We will use the Research Sentence Completion Challenge [46]. It is a database of 1040 sentences each of which has four imposter sentences and one correct sentence. Each imposter sentence differs from the correct sentence by one word in a fixed position. The deep learning system should identify the correct word in the sentence. Imposter words have similar occurrence statistics. The sentences were selected from Sherlock Holmes novels. The imposter words were generated using a language model trained using over 500 nineteenth-century novels. Thirty alternative words for the correct word were produced. Human judges picked the four best imposter words from the 30 alternatives. The database can be downloaded from Google Drive [27].
On Correctness of Normal Logic Programs

We present sufficient conditions for correctness of logic programs with negation (normal programs). We focus on the Kunen semantics, this declarative semantics is a closest one to what is implemented in Prolog (negation by finite failure) when floundering is avoided. We also recall an existing result for the well-founded semantics.
Variant-Based Equational Anti-unification

The dual of most general equational unifiers is that of least general equational anti-unifiers, i.e., most specific anti-instances modulo equations. This work aims to provide a general mechanism for equational anti-unification that leverages the recent advances in variant-based symbolic computation in Maude. Symbolic computation in Maude equational theories is based on folding variant narrowing (FVN), a narrowing strategy that efficiently computes the equational variants of a term (i.e., the irreducible forms of all of its substitution instances). By relying on FVN, we provide an equational anti-unification algorithm that computes the least general anti-unifiers of a term in any equational theory E where the number of least general E-variants is finite for any given term.
Tokamak Disruption Detection

Tokamaks are fusion machines that are under development to produce baseload power. Baseload power is the power that is produced 24/7 and provides the base for powering the electric grid. The International Thermonuclear Experimental Reactor (ITER) is an international project that will produce net power from a Tokamak. Net power means the Tokamak produces more energy than it consumes. Consumption includes heating the plasma, controlling it, and powering all the auxiliary systems needed to maintain the plasma. It will allow researchers to study the physics of the Tokamak which will hopefully lead the way toward operational machines.
Algorithmic Deep Learning

In this chapter, we introduce the Algorithmic Deep Learning Neural Network (ADLNN), a deep learning system that incorporates algorithmic descriptions of the processes as part of the deep learning neural network. The dynamical models provide domain knowledge. These are in the form of differential equations. The outputs of the network are both indications of failures and updates to the parameters of the models. Training can be done using simulations, prior to operations, or through operator interaction during operations.
MATLAB Machine Learning Toolboxes

The MathWorks sells several packages for machine learning. Their toolboxes work directly with MATLAB and Simulink. The MathWorks products provide high-quality algorithms for data analysis along with graphics tools to visualize the data. Visualization tools are a critical part of any machine learning system. They can be used for data acquisition, for example, for image recognition or as part of systems for autonomous control of vehicles, or for diagnosis and debugging during development. All of these packages can be integrated with each other and with other MATLAB functions to produce powerful systems for machine learning.
Finding Circles with Deep Learning

Finding circles is a classification problem. Given a set of geometric shapes, we want the deep learning system to classify a shape as either a circle or something else. This is much simpler than classifying faces or digits. It is a good way to determine how well your classification system works. We will apply a convolutional network to the problem as it is the most appropriate for classifying image data.
What Is Deep Learning?

Deep learning is a subset of machine learning which is itself a subset of artificial intelligence and statistics. Artificial intelligence research began shortly after World War II [35]. Early work was based on the knowledge of the structure of the brain, propositional logic, and Turing’s theory of computation. Warren McCulloch and Walter Pitts created a mathematical formulation for neural networks based on threshold logic. This allowed neural network research to split into two approaches: one centered on biological processes in the brain and the other on the application of neural networks to artificial intelligence. It was demonstrated that any function could be implemented through a set of such neurons and that a neural net could learn to recognize patterns.