Reviewerly/exHarmony huggingface dataset:
Design of Intelligent Response System for Business English (Training) Based on Improved GLR Algorithm

With the development of the Internet and e-commerce, massive amounts of data and information exist on the Internet, and valuable information is usually hidden behind complex and diverse data. For companies, it is very important to be able to analyze these data comprehensively, accurately and intelligently, and to dig out effective information from them to formulate personalized product marketing strategies for the company, so as to gain an advantage in the fierce market competition. Business intelligence is an important part of enterprise informatization, and it is a series of methods, technologies and software used to improve the performance of enterprise operations. It applies advanced information technology to the entire enterprise, through the application of data warehouse technology, data mining technology, information visualization technology and other information analysis and mining, can promote the better development of enterprise sales. Therefore, the application of business intelligence theory in the analysis of competing products can truly provide accurate, intelligent, and credible data services for corporate decision-making. Starting from the actual needs of an enterprise, this paper designs an intelligent response system for business English (training) based on the improved GLR algorithm. In the system reliability test, the data reliability of all modules is above 0.8, which improves the efficiency of business English training.
Speech Emotion Recognition System Based on Wavelet Transform and Multi-Task Learning

In the field of speech emotion recognition, great progress has been made in feature extraction and classifier construction. However, in the natural environment, speech emotion recognition is vulnerable to environmental noise. Different languages and different gender affect the accuracy of speech emotion recognition. Therefore, these problems need to be solved. Considering the noise problem, wavelet threshold denoising was proposed.After that, this paper used Google speech under the open source API to realize the transformation of voice to text.Then we designed a multi-task learning model based on Bert and LSTM. The Bert model vectorizes the text features, and then used the shared LSTM to extract the shared features. Two private LSTMs are used to extract the features of language classification in the main task and language classification in the the auxiliary task respectively. Shared features and language private features are input into the language softmax classifier through the full connection layer to realize language classification. Shared features and emotion private features are input into the emotion softmax classifier through the full connection layer to realize the classification of emotion. Orthogonal constraint training was used to make the private space contain only private features..Two experiments are designed in this paper. The first experiment is the comparison between different wavelet bases and different numbers of decomposition layers to select suitale wavelet base and the number of decomposition layer. The second experiment is the comparison between the influence of language differences on recognition accuracy and the solutions in other literature.
A Scalable Double-Chain Storage Module for Blockchain

With the growing maturity of blockchain technology, its peer-to-peer model and fully duplicated data storage pattern enable blockchain to act as a distributed ledger in untrustworthy environments. Blockchain storage has also become a research hotspot in industry, finance, and academia due to its security, and its unique data storage management model is gradually becoming a key technology to play its value in various fields’ applications. However, with the increasing amount of data written into the blockchain, the blockchain system faces many problems in its actual implementation of the application, such as high storage space occupation, low data flexibility and availability, low retrieval efficiency, poor scalability, etc. To improve the above problems, this paper combines off-chain storage technology and de-duplication technology to optimize the blockchain storage model. Firstly, this paper adopts the double-chain model to reduce the data storage of the major chain system, which stores a small amount of primary data and supervises the vice chain through an Application Programming Interface (API). The vice chain stores a large number of copies of data as well as non-transactional data. Our model divides the vice chain storage system into two layers, including a storage layer and a processing layer. In the processing layer, deduplication technology is applied to reduce the redundancy of vice chain data. Our double-chain storage model with high scalability enhances data flexibility, is more suitable as a distributed storage system, and performs well in data retrieval.
The Doctrine of Copyright Exhaustion In Software Under Indian Copyright Act: A Cursory Glance

This study provides that copyright exhaustion serves an important social function of reducing the cost of information costs. Without it, buyers will be required to waste time and resources inquiring about their ability to resell copyrighted work. Because resale rights are generally regarded as desirable by society, the law should ordinarily grant them to buyers. There are costs associated with copyright exhaustion. The major drawbacks included a decline in the incentives to innovate as well as a regressive distributive effect as a result of copyright exhaustion’s restriction on some price discrimination methods. The breadth of copyright exhaustion should be determined by the balance of these benefits and costs. This study applies the doctrinal method and explores the preferred scope of copyright exhaustion. The study argues that the copyright owner should not be prevented from exercising control over the commercial importation of copyrighted work or distributions of digital work. However, copyright exhaustion should be restricted and the copyright should not be allowed to bypass it by including magic words in their standard-form agreements. The study will examine Article 14 of the Indian Copyright Act 1957 with Art. 30 of the copyright. The present study shall briefly examine the copyright law on the applicability of doctrine in Indian copyright jurisprudence.
Comparing and Extending the Use of Defeasible Argumentation with Quantitative Data in Real-World Contexts

Dealing with uncertain, contradicting, and ambiguous information is still a central issue in Artificial Intelligence (AI). As a result, many formalisms have been proposed or adapted so as to consider non-monotonicity. A non-monotonic formalism is one that allows the retraction of previous conclusions or claims, from premises, in light of new evidence, offering some desirable flexibility when dealing with uncertainty. Among possible options, knowledge-base, non-monotonic reasoning approaches have seen their use being increased in practice. Nonetheless, only a limited number of works and researchers have performed any sort of comparison among them. This research article focuses on evaluating the inferential capacity of defeasible argumentation, a formalism particularly envisioned for modelling non-monotonic reasoning. In addition to this, fuzzy reasoning and expert systems, extended for handling non-monotonicity of reasoning, are selected and employed as baselines, due to their vast and accepted use within the AI community. Computational trust was selected as the domain of application of such models. Trust is an ill-defined construct, hence, reasoning applied to the inference of trust can be seen as non-monotonic. Inference models were designed to assign trust scalars to editors of the Wikipedia project. Scalars assigned to recognized trustworthy editors provided the basis for the analysis of the models’ inferential capacity according to evaluation metrics from the domain of computational trust. In particular, argument-based models demonstrated more robustness than those built upon the baselines despite the knowledge bases or datasets employed. This study contributes to the body ofknowledge through the exploitation of defeasible argumentation and its comparison to similar approaches. It adds to previous works, empirically enhancing the generalisability of defeasible argumentation as a compelling approach to reason with quantitative data and uncertain knowledge.
Rao-Combined Artificial Bee Colony Algorithm for Minimum Dose Path Planning in Complex Radioactive Environments

Nuclear power plant workers inevitably work in a radiation environment. Minimum dose path planning becomes an important issue in ensuring that they receive as little radiation as possible. However, a work environment with complex obstacles or narrow passages makes the problem of finding the path with the minimum dose more difficult. In this paper, we propose a meta-heuristic method to solve this problem. An algorithm called Rao-combined artificial bee colony (RABC) is proposed with a novel obstacle detour technique. The RABC algorithm combines the directional solution update method of the Rao algorithm with the traditional ABC algorithm to further improve performance. To evaluate the effectiveness of the proposed method, three hypothetical environments are simulated. In the first hypothetical environment where there are no obstacles and many radiation sources are scattered, the meta-heuristic algorithms, PSO, ABC and RABC are compared, and in the second hypothetical environment with narrow passages and obstacles, the grid-based A* algorithm and the sampling-based RRT* algorithm are used for comparison with RABC. The third hypothetical environment is a complex environment where many radiation sources and obstacles are distributed and there are narrow passages. In this case, the proposed algorithm is compared with the Dijkstra algorithm and the GB-RRT* algorithm, which is known as one of the best in obstacle avoidance. Experimental results show that the proposed method is superior in exploration and exploitation, convergence and robustness compared to other meta-heuristic algorithms as well as grid-based or sampling-based algorithms.
Upgrading Tourism Experience of Tourists by Using AR Technology

Augmented reality (Augmented Reality, referred to as AR), is a technology that calculates the position and angle of the camera image in real time and adds the corresponding image. The goal of this technology is to set the virtual world in the real world and interact with each other on the screen. This technology was first proposed in 1990. With the improvement of the computing power of portable electronic products, augmented reality is more and more widely used. This paper will share and study the AR technology to enhance the tourist experience, and strive to do relevant technical support and reserve for upgrading the tourist experience.
Micro-Content Shortlisting Using Interactive Ai Method

A cross-domain real-time shortlisting method is provided using Artificial Intelligence (AI). The method includes instantaneous identification of relevant information on the user interface (UI) like the web browser and uses a system to inject actionable buttons in an overlay on the website for shortlisting. Further, the relevant information can be shortlisted dynamically while the user is browsing. Thereafter, the shortlisted information will be available in one place as a universal list and accessible across other websites with a consistent experience.
Thought and Measures of Reforming About Database Courses in Universities

Along with the wide application and the rapid development of the database technology, the teaching of the database principle will focus on developing the students’ innovative abilities and their application skills On the basis of a comprehensive grasp of the concepts and principles, combined with the actual applications, the students will understand the development trend, and unceasingly enhance the level of the database knowledge, which has great benefits for training the comprehensive talents. In this paper, by analyzing the problems existing in the database courses, the author of this paper put forward the ideas and measures for the reform of the relevant curriculums.
A Novel Multiplier Hardware Organization for Finite Fields Defined by All-One Polynomials

In this brief, a novel, energy-efficient hardware organization for a finite-field multiplier based on irreducible all-one polynomials (AOPs) is proposed. The proposed AOP multiplier organization deploys three distinct submodules, which constitute a left-shifting network (reduction), an AND network (multiplication), and a three-input XOR tree (accumulation). Previously reported state-of-the-art implementation distributes these operations to systolic arrays, which are elegant in layout but do not yield the most efficient solution. The advantages of the proposed organization compared with those reported in the literature include reduced cost and power dissipation for a given clock frequency constraint (or increased clock frequency for a given power constraint) and the absence of bypassing problems due to fewer pipeline stages. Both the previously reported and the proposed organizations have been implemented in Verilog for three different binary-field sizes using the TSMC 90 nm standard cell library and have been synthesized for three distinct frequency targets using Cadence Genus Synthesis tool. The proposed organization achieves 18%, 31%, and 19% reduction in average leakage, dynamic capacitance, and area, respectively, compared with state-of-the-art schemes and thus can be considered for energy-efficient, compact portable systems, including wireless sensors.
A Supervised Autoencoder for Human Activity Recognition with Inertial Sensors

Human Activity Recognition (HAR) with inertial sensors is one of the most active research fields. Various machine learning algorithms have been proposed in HAR for classifying human activities. However, these methods heavily rely on the quality of hand-crafted features, requiring extensive feature engineering. Recent deep learning approaches have tried to perform training in an end-to-end manner. We propose a new learning scheme based on Supervised Autoencoder with Self-Attention ( SAE-SA ). Our main idea is two-fold: (1) Through the dimensional reduction in supervised autoencoder, our model is robust to noisy input signals sensor data. (2) We incorporate the self-attention mechanism in the classifier layer of supervised autoencoder, which focuses more to the relevant signals, and can learns the features without any knowledge of the aggregated signal data. We evaluate SAE-SA on benchmark datasets: WISDM v2.0 , and PAMAP2 . We achieve accuracy of 95.76%, and 97.60% respectively, which is the state-of-the-art results in HAR.
Towards the Improvement of UI/UX of a Human-AI Adversarial Authorship System

AuthorCAAT (Author Cyber Analysis & Advisement Tool) is a tool created to aid users in protecting their online privacy by assisting them in altering their writing style through Adversarial Authorship. There have been several iterations of AuthorCAAT that were all tested for efficiency, but not for usability. In this work, we conduct a preliminary study on AuthorCAAT-V to determine any features that need improvement to inform our iterative design decisions for an Adversarial Authorship framework JohariMAA. We plan to develop JohariMAA to adapt to the evolving authorship attribution field and be accessible to a broad audience. Our usability assessment of AuthorCAAT reveals issues involving task complexity, usability, user experience, and user interface efficiency. We discuss potential design decisions for JohariMAA planned to alleviate these issues.
Forecast of Photovoltaic Generation Using Lstm Neural Networks

Abstract - In this paper, a forecasting method for photovoltaic generation using artificial intelligence techniques with deep learning based on recurrent neural networks of long short-term memory (LSTM) is presented. Currently, solar energy is widely used worldwide owing to its high availability and significant advantages. However, this energy source exhibits a dynamic behavior that depends on weather conditions and is sometimes difficult to predict. Therefore, it is necessary to develop methods for forecasting the energy generated in PV plants. A database obtained from a 3 MW photovoltaic plant containing two years of historical data on the generated power was used. In addition, the solar irradiance, ambient temperature, temperature of the PV panels, wind speed, and wind direction were considered. Initially, the correlation between the preprocessed data were analyzed, the LSTM model implemented, and a prediction was made. The results obtained were compared with those of a recurrent neural network (RNN) model and found to be superior. Moreover, the predictions made for days with different behaviors and for different time horizons were adequate, demonstrating the effectiveness of this forecasting method.
Variable Neighborhood Search for Multi-label Feature Selection

With the growing dimensionality of the data in many real-world applications, feature selection is becoming an increasingly important preprocessing step in multi-label classification. Finding a smaller subset of the most relevant features can significantly reduce resource consumption of model training, and in some cases, it can even result in a model with higher accuracy. Traditionally, feature selection has been done by employing some statistical measure to determine the most influential features, but in recent years, more and more metaheuristics have been proposed to tackle this problem more effectively. In this paper, we propose using the Basic Variable Neighborhood Search (BVNS) algorithm to search for the optimal subset of features, combined with a local search method based on mutual information. The algorithm can be considered a hybrid between the wrapper and filter methods, as it uses statistical knowledge about features to reduce the number of examined solutions during the local search. We compared our approach against Ant Colony Optimization (ACO) and Memetic Algorithm (MA), using the K-nearest neighbors classifier to evaluate solutions. The experiments conducted using three different metrics on a total of four benchmark datasets suggest that our approach outperforms ACO and MA.
Pyidw: A Comprehensive Python Library for Geospatial Idw Interpolation

Inverse Distance Weighted (IDW) interpolation is one of earth science researchers' most used GIS tools. This journal article describes a python library named pyidw, which can be used to create beautiful IDW interpolated maps with colour bars. This python library also incorporates a new methodology where raster data (eg, elevation) can be used in combination with traditional IDW interpolation to improve accuracy. With a built-in accuracy scoring function, this library could be an all-in-one geospatial IDW interpolation toolbox for those who like to use free and open-source software in their data analysis workflow.
Harnessing Machine Learning and High Content Imaging to Automatically Quantify Malaria Parasites

In 2020 Plasmodium falciparum was responsible for over 627,000 reported malaria-related deaths, a 12% increase from the previous year. Resistance has been detected to every clinically used antimalarial, urging the development of novel antimalarials with uncompromised mechanisms of actions. High-content imaging allows researchers to collect and quantify numerous phenotypic properties at the single cell level. Using a machine learning algorithm, automated unbiased clustering of cell populations can be identified. By combining these technologies, we developed a simple method capable of robustly differentiating and quantifying P. falciparum asexual blood stages. These phenotypic properties also allow for quantification of changes in parasite morphology. Here, we demonstrate that our analysis can be used to quantify schizont nuclei, a phenotype that previously had to be enumerated manually. By monitoring stage progression and quantifying parasite phenotypes, our method can discern stage specificity of new compounds, thus providing insight into the compoundâ€™s mechanism of action.
Deployment considerations for implementing blockchain technology in the pharmaceutical industry

Blockchain is a technology that has found promise in the supply chain and logistics sector, offering an approach to establishing distributed ledger (DL) functions to centrally processed transactions in a decentralized manner. This feature may enhance regulatory compliance and product fulfillment in pharmaceutical supply chains. This chapter reviews primary applications in processing pharmaceuticals, including tracking and tracing of product necessary to assure integrity and safety, inventory management, and clinical trial efficacy. Based on industry reference models, issues emerge regarding how a DL effectively scales with transaction volume. An illustrative example demonstrates how a DL consolidates transaction volume and stabilizes variability, focusing on transactions related to exceptional situations such as those related to returns, commissioning issues, delivery disturbances, and recalls. While a DL can consolidate pairwise communication between trading partners, the net effect of this replacement diminishes as transaction rates and variability grows throughout the network. Furthermore, we demonstrate how capacity savings grow with network size, but gradually approach a maximum level. While a DL can stabilize transaction variability as transaction rates increase, the capacity savings gradually decrease. Future research areas include studies to corroborate results, explore data management, permission and access methods, partner collaboration, and implementation costs.
Emergency Remote Language Teaching During Pandemic

With the global coronavirus pandemic, educational institutions worldwide were forced to migrate to fully online instruction.This radical shift came with significant challenges for English Language Teachers (ELT) around the world.Emergency Remote Language Teaching (ERLT) had provided diverse practice on conducting online language teaching during pandemics.This paper examines the unforeseen and radical shifts of ERLT, which cover the main difference between Online Language Teaching (OLT) and ERLT, conceptual framework to develop relevant and engaging ERLT, and students' engagement during ERLT.The processes and procedures ELT devised to support their students' English language, and literacy development are reviewed through several studies conducted from different parts of the world.Such conceptual shifts entail, and the various ways ELT responded to the challenges posed are discussed and illustrated.Key lessons learned from the analysis are detailed in this paper and recommendations for teaching English at a distance.
Preliminary Study on the Mosaic Algorithm of Weather Radar Network in Sichuan Province

The detection range of the ground-based unmovable single radar is limited, which is not enough to cover the weather system above the meso-β scale, and can not track the convective system moving from one radar detection area to another. When observing with a single radar, many problems will occur due to its geometric reasons, such as static cone region, beam broadening, beam height, and beam blocking. In the regional early warning and forecast, the role of a single weather radar has some limitations. In this paper, the data of multiple radars are unified into a unified coordinate system, and the vertical linear interpolation method (NVI) is used to grid the data of multiple radars. Finally, the distance-weighted method is used to fuse the spliced grid data to realize the high-precision weather radar network mosaic. The networking experiment with eight Doppler weather radars in Sichuan shows that the networking mosaic algorithm based on vertical linear interpolation method and distance weight fusion method can achieve high precision and reliable mosaic effect, which is beneficial for the better observation of the mesoscale weather system.