Reviewerly/exHarmony huggingface dataset:
Semantic comparison of texts by the metric approach

Abstract A novel approach to the semantic comparison of texts based on the metric method to calculate semantic distances between lexical units is put forward. The supplementary semantic information is provided through semes of the words composing the texts, or through their semantic fields. The proposed method takes into account semantic polarity and yields, for two paraphrase sentences, more feasible results than the conventional approaches based on word occurrences. The described approach may be useful for linguistic theory as well as for a variety of Natural Language Processing tasks based on supervised learning that require semantic information: computer lexicography, semantic analysis, information search and retrieval, document classification, text summarization, and understanding machine translation and others.
Errors of Identifiers in Anonymous Databases: Impact on Data Quality

Data quality is essential for a correct understanding of the concepts they represent. Data mining is especially relevant when data with inferior quality is used in algorithms that depend on correct data to create accurate models and predictions. In this work, we introduce the issue of errors of identifiers in an anonymous database. The work proposes a quality evaluation approach that considers individual attributes and a contextual analysis that allows additional quality evaluations. The proposed quality analysis model is a robust means of minimizing anonymization costs.
Identification of Critical Subgraphs in Drone Airways Graphs by Graph Convolutional Networks

Our original aim was to study the control of drones in a congested airspace that may be structured in airways much similar to road or subway traffic networks. The question posed was if it is possible to identify subgraphs whose congestion that may lead to the blockade of the entire network, i.e. if there are dominant subgraphs in the network. We resort to semi-supervised trained Graph Convolutional Networks (GCNs) to formulate and solve the problem. For traffic networks structured as streets or subway lines, the notion of subgraph dominance is very intuitive, because we can postulate that if the labeling of the graph assigns a line label to many nodes belonging to other lines, then the flow in this line should affect these nodes and their corresponding lines. We have carried out experimental work over a manageable network, the Vienna subway network, achieving high and robust accuracy results in the semi-supervised labeling process. However, line dominance results are less robust, they seem to be highly influenced by the experimental setting given by the actual seed nodes provided to networks, which calls for further experiments and analysis in order to try to find stable responses.
The Impact of Content Deletion on Tabular Data Similarity Using Contextual Word Embeddings

Table retrieval is the task of answering a search query with a ranked list of tables that are considered as relevant to that query. Computing table similarity is a critical part of this process. Current Transformer-based language models have been successfully used to obtain word embedding representations of the tables to calculate their semantic similarity. Unfortunately, obtaining word embedding representations of large tables with thousands or millions of rows can be a computationally expensive process. The present work states the hypothesis that much of the content of a table can be deleted (i.e. rows can be dropped) without significantly affecting its word embedding representation, thus maintaining system performance at a much lower computational cost. To test this hypothesis a study was carried out using two different datasets and three state-of-the-art language models. The results obtained reveal that, in large tables, keeping just 10% of the content produces a word embedding representation that is 90% similar to the original one.
Small Vessel Detection in Changing Seaborne Environments Using Anchor-Free Detectors on Aerial Images

We propose an anchor-free object detector to accurately locate small vessels in changing seaborne environments for search and rescue operations using aerial images. Using the YOLOX architecture, we have achieved high accuracy and real-time inference on the SeaDronesSee dataset. In order to face the high imbalance present in the dataset, the variations of the dataset and a weighted loss have been proposed and implemented. Additionally, the proposed method has been tested on unseen images from similar datasets achieving promising results and proving the robustness of the solution.
Applying Deep Q-learning for Multi-agent Cooperative-Competitive Environments

Cooperative-competitive social group dynamics may be modelled with multi-agent environments with a large number of agents from a few distinct agent-types. Even the simplest games modelling social interactions are suitable to analyze emerging group dynamics. In many cases, the underlying computational problem is NP-complex, thus various machine learning techniques are implemented to accelerate the optimization process. Multi-agent reinforcement learning provides an effective framework to train autonomous agents with an adaptive nature. We analyze the performance of centralized and decentralized training featuring Deep Q-Networks on cooperative-competitive environments introduced in the MAgent library. Our experiments demonstrate that sensible policies may be constructed utilizing centralized and decentralized reinforcement learning methods by observing the mean rewards accumulated during training episodes.
Theoretical Analysis of Null Foley-Sammon Transform and Its Implications

Null Foley-Sammon Transform (NFST) has received increasing attention in the machine learning and pattern recognition literature. NFST finds a discriminative nullspace where all samples of the same class get mapped into a single point. It has a closed form solution and is free of parameters to tune. NFST has been leveraged in many areas including novelty detection, person or vehicle re-identification and achieved state-of-the-art results. Motivated from its attractive properties and its effectiveness in wide range of applications, in this paper we focus on the theoretical analysis of NFST. In previous literature, NFST was shown to exist in small sample size (SSS) case. We first prove that NFST can exist in non-SSS case also, under certain conditions. Thereby, we extend the domain of applicability of NFST to a more general case. Secondly, we perform analysis of the singular points of NFST, revealing important insights on their identities and existence. Thirdly, we show the theoretical relation between NFST of SSS data and NFST of the non-SSS data obtained by PCA. Fourthly, we show that this theoretical relation can be exploited to obtain an efficient algorithm for computing NFST on high dimensional SSS data. Finally, we perform extensive experiments to validate our theoretical analysis.
Generation and Research of Online English Course Learning Evaluation Model Based on Genetic Algorithm Improved Neural Set Network

The rationality and timeliness of the comprehensive results of English course learning quality are increasingly important in the process of modern education. There are some problems in the scientific evaluation of English course learning quality and teachers' own English course learning, such as the need for proper adjustment and improvement. Based on the improved network theory of genetic algorithm, this paper takes an online English course learning quality evaluation model and uses MATLAB 7.0 to write the graphical user interface of the neural set network English course learning quality prediction model. The model uses the genetic algorithm of adaptive mutation to optimize the initial weights and values of the neural set network and solves the problems of prediction accuracy and convergence speed of English course learning quality evaluation results. Simulation experiments show that the neural set network has a strong dependence on the initial weights and thresholds. Using the improved genetic algorithm to optimize the initial weights and thresholds of the neural set network reduced the time for the neural set network to find the weights and thresholds that meet the training termination conditions, the prediction accuracy was increased to 0.897, the prediction accuracy was 78.85%, and the level prediction accuracy was 84.62%, which effectively promoted the development of online English course learning in colleges and the continuous improvement of teachers' English course learning level.
Retro-reflective target with high accuracy for industrial photogrammetry

Abstract Retro-reflective targets (RRTs) are important for obtaining the coordinates of the surface points of objects measured using industrial photogrammetry systems. Targeting the low measurement accuracy caused by the poor roundness of the conventional RRT, a novel RRT was designed in this study by covering a die-cut slice with round holes and high roundness on a glass bead reflective film. The roundness of this novel RRT was on average 0.03 mm higher than that of the conventional RRT. The accuracy of the novel RRT was evaluated in terms of both internal and external coincidence accuracies. The measurement repeatability accuracy of the novel RRT was improved by 25.0% compared with that of the conventional RRT. The thinner the die-cut slice thickness, the higher the repeatability in a certain range. Similarly, the plane fitting, flatness measurement, and standard length measurement accuracies were improved by 30.7%, 34.1%, and 28.1%, respectively.
A Rational Mining Strategy for Proof-of-Work Consensus Algorithms

To maintain a secured, universal state of a blockchain, Proof-of-Work consensus algorithms economically incentivize miners to compete for block creation through hashing-based challenge solving. Nowadays, the default mining strategy consists in including as many transactions as possible in a block so as to maximize the block reward. Unfortunately, this strategy also maximizes the risk of block orphaning. In this work, we propose a rational mining strategy that carefully balances the trade-off between the block reward and the risk of block orphaning. The strategy is designed so that the chance to get a block reward is higher than the default strategy chance. Furthermore, the strategy is flexible as it provides a degree of freedom of how much larger this chance should be. We analytically study the long-term economic advantage of the proposed approach and derive the condition under which the long-term reward is higher than the one of the default strategy. Finally, we validate the proposed strategy based on a case study analysis method against Bitcoin and Ethereum and show that the higher the risk of block orphaning the more convenient the strategy happens to be.
Blockchain-based Data Access Environment for Disaster Risk Reduction<sup>1</sup>

This paper outlines an opportunity to apply blockchain technology to enhance access to the universe of data needed to assess risk and guide allocation of resources for Disaster Risk Reduction (DRR). The project aims to develop and implement a blockchain-based governance model, a Blockchain Based Data Access Environment (BDAE) for conditional data access and curation. A two-layered architecture creates an incentivized mechanism for data sharing and data quality services.
A survey on Automatic Query Optimization Approaches in Multi Store Systems for Big Data Analytics

Relational Database Management System (RDBMS) was the most used storage and query processing engine for data warehousing applications till the arrival of Bigdata stores. Enterprises are recently adopting a multi store system with combined use of Bigdata stores and RDBMS. Bigdata stores are used for exploratory queries and gathering business insights. RDBMS is used for business reporting. Though, Multi storage systems improves the query processing capability by executing it over multiple stores it is challenging to optimize the query execution considering queries on both structured and unstructured data for big data analytics. This work surveys the existing methods for query optimization in multi storage systems. The objective is to identify the challenges and shortcoming in the existing methods on query optimization over multi data stores.
Insurance Value Chain Chatbot for Farmers

Agriculture being the major occupation carried out in India, the Government has deployed various farming and insurance schemes for the benefit of the farmers. Based on research it has been observed that only 50-55% of the total farmer population have taken up insurance or have the correct understanding and knowledge. The current research and technologies proposed and developed involve providing new farmers with knowledge about how to take up farming as an occupation and provide answers to the various farming related queries.Providing a smooth farming experience with all the required information to start an occupation in it is great, but there also needs to be an understanding on how they can save or protect the farm from any accident or a misfortune that may happen in future. This is where insurance for farmers come into picture and needs to be addressed to make it more aware among the masses. The current research does not mention the importance on farmer insurance and is a limitation.The system developed as a result of this research is analyzed conceptually to address the current system limitations and provide an insurance automated system.
Smart Medical Health Card for Hospital Management

The use of information technology has become a common place in healthcare. The increase in the number of patients in need of ongoing care is a responsibility for medical professionals. Today the patient monitoring system and wireless medical equipment are used to monitor patients, but nevertheless patients have to stay inside the wireless coverage area. This real-time information presents an ongoing challenge to the emergency community. In these emergencies as unconscious and unaccompanied patients, giving emergency physicians or doctor an accurate and updated medical history of a patient can be the difference between his/her life and death[1]. In this smart health card project patient we are tracking and monitoring of patient history. The system monitors patient’s vital signs such as past history, blood group, past medicine history bills. The patient data is stored in one RFID card. In this smart card project we are using RFID technology. This RFID technology is currently very useful in health care system due to its increased performance, lower cost, high efficiency and easy-to-use skills. Physicians can track all information about their patients using a dedicated system or model. Also, they can ask using an established web server. The integration of smart card system used in existing automation system should be important part of the hospital management as far as it benefits both the hospital management and patients.
Social Distance Monitoring to Enhance the Safety from COVID-19 Infection

The best precaution in the COVID-19 pandemic period is social distancing. Despite being aware of these, people often violate social distance and take down their masks in public places, knowingly or unknowingly. This unawareness is the primary reason for public places like retail shops, bus stops, public transport, educational institutions, hospitals, and clinics to become increasingly inaccessible and COVID-19 hotspots. It would be practically impossible for anyone to make sure everyone is following social distancing in a public place; a human will not be able to see beyond his field of view. An automated CCTV-based real-time system could help in monitoring the crowd. The proposed model takes a video stream as input, segments people using a CNN-object detection algorithm (YOLO), anchors objects' locations, and finds the distance between them. The system then marks bounding boxes around people who do not follow social distancing. Using pixel coordinates of violating people can accurately mark and alert security personnel or display wherever social distancing is violated.
User Centric Predictive Model for Mixed Reality Applications

An improved understanding of the underlying computations that involve the users, termed user-centric computing is the need to evolve towards a more formal and systematic process to design and evaluate interactive software for both traditional and nontraditional interactive systems to enhance the user experience. User-centric models direct the designers and developers to design and evaluate interactive systems so that it helps in both the design as well as in evolution. Mixed Reality (MR) is a promising technology and deals mostly with nontraditional user interaction modes in real-world compared to other related technologies. Developing a predictive model for AR/MR applications visualized through smart glasses is intricate and depends on the dissimilar screen components. This paper mainly focuses on the predictive model for immersive technology applications, Fitt’s law, which is essentially the extended version of the predictive models.
Named Entity Recognition for Public Interest Litigation Based on a Deep Contextualized Pretraining Approach

The named entity recognition (NER) in the field of public interest litigation can assist prosecutors in handling cases and provide them with specific entities in making legal documents. Previously, the context-free deep learning model is used to catch the semantic comprehension, in which the static word vector is obtained without considering the context. Moreover, this kind of method relies on word segmentation technology and cannot solve the error transmission caused by word segmentation inaccuracy, which brings great challenges to the Chinese NER task. To tackle the above issues, an entity recognition method based on pretraining is proposed. First, based on the basic entities, three legal ontologies, NERP, NERCGP, and NERFPP are developed to expand the named entity recognition corpus in the judicial field. Second, a variant of the pretrained model BERT (Bidirectional Encoder Representations from Transformer) called BERT-WWM (whole-word mask)-EXT(extra) is introduced to catch the text character-level word vector hierarchical and the context bidirectional features, which effectively solve the problem of task boundary division of named entities. Then, to further improve the model recognition effect, the general knowledge learned from the pretrained model is used to fit the downstream neural network BiLSTM (bi-long short-term memory), and at the end of the architecture, CRF (conditional random fields) is introduced to restrict the label relationship. Finally, the experimental results show that the proposed method is more effective than the existing methods, which reach 96% and 90% in the F1 index of NER and NERP entities, respectively.
Performance Evaluation of Multiagent Reinforcement Learning Based Training Methods for Swarm Fighting

In this paper, we conducted a performance evaluation of two multiagent reinforcement learning based training methods for swarm fighting, namely, the multiagent reinforcement learning (MARL) training method, and the combined multiagent reinforcement learning and behavior cloning (MARL-BC) training method. The behavior cloning expert is taken from some well-trained model in the final steady phase by the MARL training method. From the perspective of winning rate, the performances of these two different training methods can be divided into three phases. In the first phase, learning progresses slowly for both these two training methods. As the model trained by the MARL training method grows stronger, the experience of the behavior cloning expert gradually becomes useful, and the second phase kicks off where the MARL-BC training method takes obvious advantage. Surprisingly, the advantage of the MARL-BC training method will disappear as the learning progress goes on because in this final phase the expert of the behavior cloning training method can no longer offer the right strategy in presence of the ever changing environment and opponent.
Application of Challenging Learning Based on Human-Computer Interaction under Machine Vision in Vocational Undergraduate Colleges

Science and technology have progressed in recent years, the deepening of talent education, a challenging learning method of human-computer interaction has gradually emerged. Human-Computer Interaction (HCI for short) is the communication and interaction between humans and machines. This essay aims to apply the challenging learning combined with HCI to vocational undergraduate colleges. The GMM (that is Gaussian mixture model algorithm, commonly used in image recognition or speech recognition, etc.) algorithm is proposed in this essay to recognize students' actions. The effect of HCI is achieved by feeding back the recognized actions to the system. This essay selects 200 students from a vocational undergraduate college for challenging learning (that is, a comprehensive teaching method aiming at students' autonomous learning by stimulating students' interest in learning). The challenging learning designed in this essay is divided into 15 weeks, the task chain contains a total of 196 tasks, and the learning time is 138 h. This essay analyzes the application effects of liberal arts, male and female students, and different grades. The results show that the overall average completion rates of learning tasks for freshman, sophomore, and junior students are about 70%, 75%, and 85%, respectively, and the overall average scores for challenging learning are about 70, 78, and 83. The overall completion rate of weekly tasks for boys and girls is about 68% and 70%, and the overall average score is about 70 points and 75 points. The weekly task completion rate of liberal arts students is generally above 75%, and the overall average score is about 70 points. The overall completion rate of science students is less than 75%, and most of the learning scores are higher than 75 points. In addition, the average accuracy of the GMM algorithm for face and gesture recognition is 90% and 87%. The average frequency of students using HCI is about 320 times a day; the average score of students' experience effect of HCI is about 80 points. It may be stated that the HCI demanding learning strategy proposed in this study worked well and has achieved satisfactory learning results in the application of vocational undergraduate colleges.
Low-Latency Speculative Inference on Distributed Multi-Modal Data Streams

While multi-modal deep learning is useful in distributed sensing tasks like human tracking, activity recognition, and audio and video analysis, deploying state-of-the-art multi-modal models in a wirelessly networked sensor system poses unique challenges. The data sizes for different modalities can be highly asymmetric (e.g., video vs. audio), and these differences can lead to significant delays between streams in the presence of wireless dynamics. Therefore, a slow stream can significantly slow down a multimodal inference system in the cloud, leading to either increased latency (when blocked by the slow stream) or degradation in inference accuracy (if inference proceeds without waiting).