Reviewerly/exHarmony huggingface dataset:
Improved Bat Algorithm for Solving Traveling Salesman Problems

To solve traveling salesman problems more efficiently, an improved parallel bat algorithm (IPBA) is proposed in this research. The main improvement measures are as follows. First, to be suitable for solving discrete problems, the relevant operations of the algorithm are discretized. Then, a chaotic initialization based on the immune concentration concept is presented to obtain a more competitive initial population. In addition, decreasing inertia weight and stagnate variation strategy are adopted to balance the global and local search of the proposed algorithm. Especially, parallel strategy is introduced to divide the whole population into two subpopulations of exploration and exploitation, which adopts different inertia weights and stagnate variation strategies to evolve. Regular information exchange between subpopulations is used for the advantages of parallel strategy and improves the overall performance of the proposed algorithm as well. At last, the proposed IPBA is adopted to solve typical traveling salesman problems with different scales, and the comparison and analysis of the optimal results verify the feasibility and effectiveness of the proposed algorithm.
Research on Knowledge Graph Oriented Algorithm for Extracting Relationship of Tourism English Text Information

For the nested use of entity objects and overlapping associations in extracting tourism English text, a joint extraction model BAMRel is proposed using the dual affine attention mechanism for obtaining entity object-related data information. This model uses the dual affine attention mechanism with common encoding layer parameters to form an information matrix of the entity object identification and the association extraction. At the same time, association extraction is integrated into the entity-type information system, enhancing the role of association extraction and the interaction between the two types of tasks. Due to the use of remote monitoring and manual verification to extract TFRED from the tourism English text information relationship dataset, the F-1 value of BAMRel mode in this dataset exceeds 91.8%, effectively overcoming association nesting and data duplication. To demonstrate the robustness of the pattern, comparative experiments are conducted on the DuIE dataset and mainstream joint extraction patterns, and the BAMRel pattern achieves the highest F-1 value of 8.2%.
Case Analysis System Based on SSM Framework

With the rapid development of the Internet, the number of crimes committed through the Internet is also increasing. In online gambling cases, pyramid schemes, and other cases, the leaders or associates behind them often get away with the law although the police can catch front-line suspects. The traditional approach of the police to find hidden suspects is collecting evidence through electronic devices such as the mobile phones of the suspect captured. By using MS Excel to perform data intersection processing, potential suspects can be identified. However, this method is inefficient and prone to errors when analyzing a large number of data tables. Thus, using C/S page queries and combining them with communication data obtained from electronic forensics and travel information, we analyzed most cases with the intersection of data tables, which can be deployed and used everywhere.
Evaluation of Teaching Effectiveness of College English Courses Based on GA Algorithm

To improve the inaccuracy of current college English teaching level assessment, a teaching level assessment model using GA algorithm designed radial basis function neural network was designed. Firstly, the teaching level assessment index was screened through principal component analysis, and the radial basis function neural network model was determined. Then, the weight of the radial basis function neural network was adjusted through the GA algorithm. Experiment results showed that the algorithm efficiently assessed the level of college English courses.
Research on the application scenarios and supporting technologies of digital technology in the power industry

At present, digital technology has become an inevitable trend in the development of the power industry. However, there is a lack of research on the impact of digital technology on the power industry from the full life cycle perspective. In this paper, it is pointed that the effect is mainly reflected in intelligent power production, power equipment fault diagnosis and prognosis, stability operation of the power grid, intelligent electricity trading and marketing, and the power industry standard digital transformation. In this paper, the application scenarios of the digital technology in the power industry are detailed analyzed and the relevant supporting technologies are introduced. Some examples are shown to support the point of view. This paper comprehensively elaborated the application blueprint of digital technologies in the electric power industry and provided direction for the digital transformation of the electric power industry.
Optimizing Parameters of Support Vector Machines Using an Enhanced Whale Optimization Algorithm

To enhance the parameters of the kernel function in the conventional support vector machine (SVM) - σ and penalty factor C - an advanced whale optimization algorithm (IWOA) is introduced within this article to optimize the SVM parameter model (IWOA-SVM). The IWOA algorithm is employed to augment the optimization capability of the original whale optimization algorithm, focusing on three key aspects: Firstly, the chaotic circle mapping technique is utilized to produce the initial positions of the initial whale population, which serves as a foundation for population diversity during the algorithm's global search process; Secondly, an adaptable weight parameter is integrated into the spiral ascent phase of the whale, which reinforces the local exploration capability of IWOA, accelerates its rate of convergence and augments the precision of the algorithm; Lastly, the Cauchy mutation perturbation is employed to alter the current optimal solution, thereby averting the algorithm from being confined to a local optima state. The optimization of parameters for the SVM kernel function is achieved through the Improved Whale Optimization Algorithm, by tuning the kernel function's parameters such as σ and penalty factor C, and then verified on the UCI dataset. In comparison to conventional SVM, Particle Swarm Optimization SVM, Genetic Algorithm Optimization SVM, and Original Whale Algorithm Optimization SVM, IWOA-SVM demonstrates the highest classification accuracy, indicating its effectiveness as an SVM parameter optimization algorithm.
Tibetan Speech Recognition Based on Multipath Convolutional Neural Network

In recent years, end-to-end automatic speech recognition (ASR) has been widely used. Many researchers have studied and improved convolutional neural networks (CNNs), but most of them are improved from the depth direction, ignoring some detailed features in the width direction. Therefore, this paper proposes a MCNN-CTC based method for Tibetan speech recognition. The multipath convolutional neural network (MCNN) acquires more detailed features by increasing the width of the network. In order to study the effect of the depth and width of the model on speech recognition rate, this paper studies MCNN-CTC with DCNN-CTC as the baseline model. The results show that compared with DCNN-CTC, the relative error rate of MCNN-CTC decreases by 7.72% when syllables are used as modeling units. When the word was used as the modeling unit, the relative error rate decreased by 7.79%, which verified the validity of the model.
Prediction of Trend of Digital Talent Demand in China by BP Artificial Neural Network Model

With the continuous integration of the digital economy with traditional industries, the demand for digital talents in China has also significantly increased. Therefore, predicting the trend of digital talent demand in China is important. Using the linear regression method and BP artificial neural network model and data from listed ICT companies in China, we predict the demand trend for digital talents in China. The results show that the demand for digital talents, intermediate and senior talents, and low-level laborers in China has steadily increased. The scale of enterprises is positively correlated with the total employment volume of the digital industry, while the tax burden and financing environment of regions is negatively correlated with the employment volume of the digital industry, which is not conducive to the increase of talent demand in the digital industry.
BERT-Based Ensemble Learning Approach for Sentiment Analysis

Sentiment Analysis is a fundamental problem in social media and aims to determine the attitude of a writer. Recently, transformer-based models have shown great success in sentiment analysis and have been considered the state-of-the-art model for different NLP tasks. However, the accuracy of sentiment analysis for low resource Languages still needs improvements. In this paper, we are concerned with sentiment analysis for Arabic documents. We first applied data augmentation techniques on publicly available datasets to improve the robustness of supervised sentiment analysis models. Then we proposed an ensemble architecture of Arabic sentiment analysis by combing different BERT models. We validated these methods using three available datasets. Our results showed that the BERT-based ensemble method achieves an accuracy score of 96%.
Design of Large-Scale Electronic Information Intelligent Cache System Based on Knowledge Map

Knowledge atlas is a knowledge representation system. The goal of this project is to design a large-scale intelligent cache system, which will be able to store, retrieve and update information in real time. It can be used to represent any type of knowledge, and it is not limited to the field of science or engineering. Knowledge maps can be used to represent concepts, problems, solutions, etc., in a variety of ways, such as: representing general information about topics; represents specific information about the subject; represents the relationship between concepts; indicates the relationship between a concept and its attributes (e.g., the relationship between people); create a new concept from an existing concept (for example, create a new class from an existing class). In this article, we will see how to design a large electronic information intelligent cache.
Structure Feature Extraction for Hierarchical Alarm Flood Classification and Alarm Prediction

Alarm flood classification and alarm prediction are significant ways to assist the on-site operators to manage alarm floods and maintain process safety. The two tasks are interdependent considering the decisive role of different alarm floods on the arising alarms. To give comprehensive consideration of both, this work proposed a hierarchical strategy for alarm flood classification and alarm prediction leveraging the structure feature of alarm floods. The structure feature aims at revealing the sparse causal dependencies among alarm variables. It is achieved by a deep learning model under the guidance of a designed objective function that probabilistically parametrizes causal dependencies with sparsity constraint. Due to its interpretable physical meaning, desirable robustness and discriminate properties are achieved, allowing a win-win situation for both tasks. Based on the structure features, the hierarchical strategy is given, where an overall classifier is built while prediction models are trained for each category. The classifier trained by structure features is predisposed to generate satisfactory early classification results, enabling timely prediction. For the prediction, the structure features are also used to incorporate temporal features to achieve better performance. Experimental results illustrate the interpretability of structure features and show the feasibility of the proposed hierarchical strategy. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —During alarm floods, the alarm patterns are different than usual and the generic prediction model may fail. The focus of this study is to achieve a win-win situation for both alarm flood classification and prediction, thereby providing comprehensive information required for handling alarm floods. Considering the arising alarm is strongly affected by the type of current alarm flood, a hierarchical alarm flood classification and alarm prediction strategy is given. It exploits the essential characteristic of alarm interactions in alarm floods to generate robust and early classification results, allowing timely predictions by category. In this way, the performance of both tasks can be guaranteed. The proposed method requires labeled historical alarm flood data.
F-DDPT: An Efficient Fuzzy-Based Automated Preprocessing Technique to Support Explainability

In the real world, the machine learning techniques usually handles datasets with binary values and the problematic situations arise when numerical datasets to be converted to binary values using sharp edges of membership. Fuzzy modeling is used for the conversion of the numerical quantitative datasets to binary values, in order to avoid any loss of valuable information due to the sharp partitions, specifically at boundary partitions. But, before any machine learning techniques is used with the domain original dataset, i.e., crisp dataset has to be converted into fuzzy dataset which must project the expert way of viewing and analyzing the data is required. This research paper describes an algorithm called F-DDPT to proceed this preprocessing in the perspective of domain expert. The paper contains illustration of the implementation of F-DDPT algorithm with spatial domain dataset with expert driven information. This algorithm involves using fuzzy modeling techniques to change to the fuzzy form of the unique dataset. Eventually, the fuzzy dataset is made available in a standard way such that they can support machine learning techniques in explainability. This work also proves that the F-DDPT is much more efficient and faster than other preprocessing techniques like FCM, etc. This F-DDPT algorithm can be used as efficient preprocessing technique for other domain dataset with expert driven information.
Application of Computer 3D Printing Technology in Trace Inspection

3D printing technology is a new high-tech with a unique operation mode. Although 3D printing technology has broad application prospects in various fields, it is necessary to further strengthen its research. The application of computer 3D printing technology in trace detection is the process of creating a three-dimensional model or digital representation of an object. The main purpose of this technology is to find out the defects, missing parts and other problems in the inspected products. It can be used in manufacturing, quality control, design and more applications. This process uses computer aided design (CAD) software to help create accurate models from scratch. CAD software has all necessary tools, such as measuring tools, measuring equipment, etc., which are helpful to obtain accurate measurement values of scanned objects.
PBFT Consensus Algorithm Based on Blockchain Intellectual Property Protection

With the prosperity and development of the cultural industry, more and more original content in digital form has emerged online and offline. At present, most of the existing methods of intellectual property protection are offline registration in authoritative management institutions, which has the following shortcomings: first, for many network creators, the process of applying for registration is extremely cumbersome and expensive. The second is that the current efforts to protect intellectual property rights are not enough. The centralized form of institutional registration causes the opacity of copyright information. Third, it is difficult to define the infringement on the network, the punishment is weak, and it is very difficult for the author to prove. Blockchain technology happens to have the characteristics of decentralization, transparency, openness and tamperability. At present, it is widely used to solve all kinds of trust problems. PBFT consensus algorithm is based on blockchain intellectual property protection. It is a new consensus algorithm, which can solve the problem of copyright infringement and protect the rights of authors. PBFT consensus algorithm aims to prevent plagiarism, and it will be directly used in the development of smart contracts. In addition to solving the problem of copyright infringement, PBFT also solves other problems, such as plagiarism; Illegal distribution; Unauthorized use; And so on, so that users can enjoy their content without any restrictions.
Sign Language Recognition with Machine Learning

Communication is the basic act which is done by human beings to transfer, sharing or exchange information, ideas, or feelings with others. There are mainly five types of communication verbal, non-verbal, listening, written, and visual, but mostly we use speaking and listening for communication. Communication is a basic way for human being to express their feelings and thoughts to others, in our country approximately 63 million people are suffering from deaf and hard to hearing. According to World Health Organization (WHO) over 5% of the world’s population is suffering from hearing loss (432 million adults and 34 million children). Lots of people in over the world are suffering from dumbness or speaking disability. All over the world sign language is used to communicate between the normal and the people with hearing and speaking disabilities. But, a maximum number of our population is unaware of sign language and how to talk with deaf and dumb people. In recent time, deep learning and computer vision techniques has improved greatly, and motion and gesture recognition using deep learning and computer vision-based techniques are creating milestones. The main focus of this work is to make a system that will first take the visual inputs (video), and then it will extract the needed features, then the model will recognize the features from the video or visual input and provides text and audio-based output which will be understandable by normal humans. Through this AI and machine learning technology will play a role in filling the communication gaps between normal people and people with hearing and speaking disabilities.
Blockchain-Based Clean and Green Energy (Pico Hydel Power) Monitoring System

This article introduces a peer-to-peer (P2P) and open-source electric energy monitoring system. It facilitates P2P monitoring data in a centralized system that generates data for sharing of Peer nodes. A system is proposed that provides a real-time data acquisition system for monitoring the health and performance of remotely located clean and green generating units, like the Pico Hydel Power, Wind Power, Livestock Power, and Solar Power generating units. These clean and green energy generating units produce zero carbon footprint. The monitoring and maintenance can be done through an interface using a private Ethereum blockchain. The Ethereum blockchain is used for a smart contract and Python programming is used to record or monitor information on the web interface through a secured blockchain system. The Internet of Things (IoT) portal is used for monitoring and controlling the generating energy units. Energy monitoring and other maintenance data are collected and pre-processed by using “Raspberry Pi 0 w/Raspberry Pi 0 2w” boards along with field sensors, which consist of an external real-time clock, generated voltage sensor, load current sensors, and other environmental monitoring sensors essential for the maintenance of the generating unit. In this article, an open-source, decentralized P2P electric energy monitoring system is proposed which is built on top of blockchain technology and the Internet of Things (IoT). The hardware setup consists of current sensors, a voltage sensor, an external real-time clock, a built-in Wi-Fi unit, a small “Raspberry Pi” Board, and other environmental monitoring sensors. Finally, it forms the Edge Node. The “Message Queuing Telemetry Transport (MQTT) protocol” can be used over a local network for data transfer. “Raspberry Pi 0” can act as an MQTT client and “Raspberry Pi 4” can act as an MQTT broker. Finally, it forms the Fog node, which acts in a cluster format. All these Fog node clusters are also linked to Desktop PC, which is termed a Cloudlet Server. The Python interface method is implemented to connect the Edge Node cluster to the Fog Node. The Cloudlet Server with the cloud interface is developed using open-source library programs (mainly Python-based).
Parameters Optimization of YOLO Algorithm for Vehicle Detection in SMIT Campus

Vehicle detection is an important method needed for autonomous administration of vehicle traffic control. It is the primary initial step often employed for operations like vehicle overspeed detection and other traffic movement violations. Monitoring vehicle activity is very important in crowded and widespread public roads of large organizations and institutions. It is important to employ efficient and optimized algorithms requiring low hardware resources and configurations. The software program must be fast and must work as a standalone system for consistency in its operation. YOLO algorithm is a lightweight method faster than most other CNN-based algorithms having acceptable performances for large objects. The paper used YOLO algorithm in MATLAB software for vehicle detection in a campus environment of Sikkim Manipal Institute of Technology (SMIT) campus. It was able to detect vehicles very effectively with an accuracy up to 95% with over 217 samples of training images.
Ethical Self-driving Cars: Resolving Conflicting Moral Decisions Among Groups of Vehicles

In unavoidable accident situations, self-driving cars (SDCs) find themselves to be the judge, and potentially the executioner, of a situation involving human and animal life. Equipping self-driving cars with ethical frameworks to be able to take a decision in such situations is not enough. Conflicts will inevitably occur on what should be done due to the different perceptions of each car and their potentially conflicting underlying moral codes. This paper describes an argumentation-based framework to allow cars in a group to engage in a human-like dialogue in order to resolve ethical conflicts in unavoidable accident scenarios. After a dialogue is completed, the decision with the strongest argument is chosen using traditional concepts of argumentation. Furthermore, due to the lack of absolute claims, a reasoning mechanism that deals with uncertainty, Dempster-Shafer Theory of Evidence (DST), is used to enhance the final decision. Results have shown the correctness of the proposed framework in accident scenarios using computer simulations.
Challenges and Solutions in Large-Scale News Recommenders

This paper describes the main challenges and solutions adopted in large-scale production news recommenders in Grupo Globo, the biggest Latin American mass media group. Its vertical information portals reach 100 million unique daily users, playing an important role in content distribution in Brazil. News recommenders encompass additional challenges in comparison to recommenders from other domains, especially regarding recency and cold-start. In this context, recommendation metrics and quality are highly impacted by the short lifetime of new items. This work shares the main challenges we have been facing in providing news recommendations in large-scale production environments. We also discuss solutions we have been applying so far to deal with these scenarios as well as some case studies evaluating the proposed approaches, where online metrics are tracked by means of an in-house AB testing platform.