Reviewerly/exHarmony huggingface dataset:
A Fuzzy-Based Hybrid Approach for Estimating Interdisciplinary Learning Efficiency

Students, industry, and departments of civil engineering are engaging interdisciplinary learning techniques to promote sustainability and meet the urgent requirements of human development. However, only a few pieces of literature discussed the learning efficiency and educational feedback of this trend. This may be resulted from the difficulty in estimating subjective engagement. Hence, this study surveyed 173 data from 6 departments that provided interdisciplinary courses based on three dimensional trapezoidal fuzzy numbers and Likert scale. The questionnaire factors were modified from the useful and common scales in Science, Technology, Engineering, and Mathematics (STEM) education. Existing studies tend to estimate students’ learning efficiency based on the hybrid approach of Data Envelopment Analysis (DEA) and Principal Component Analysis (PCA) methods. However, the meaning of variables may be changed in the PCA’s procedures of axis transformation. Hence, a new fuzzy DEA-Mahalanobis distance approach was proposed to solve this problem. Based on the student gains in the sustainability courses, three groups were clustered and compared. Feedback for students was suggested to improve learning strategies. The sensitivity analysis showed that “effective teaching practice” and “learning from other open courses or YouTube” are key factors in increasing learning efficiency in the sustainability courses for civil engineering students. Therefore, this paper provided a useful and easy approach to improve learning strategies for civil engineering students in sustainability education.
Resolving the message riddle: A multi-pronged approach to infer trouble tickets

Today's IT systems heavily rely on IT support for smoother and faster operations. Any application or infrastructure issue is reported by tools or end-users in the form of a trouble ticket. The information about actual issue is hidden inside these ticket descriptions and is provided in different ways. In this paper, we address the problem of extracting the issues from these ticket descriptions. We break the problem into different sub-problems each presenting different challenges and hence requiring different solutions! We present our experience of applying theory into practice by presenting a real-world case-study.
The East Kainji languages of Central Nigeria

The paper is an overview of current scholarship on the East Kainji language group of Central Nigeria. It reviews the existing published and manuscript sources and describes recent research, as well as the development of orthographies for some languages. Many East Kainji languages are severely threatened and some have gone extinct within the period under review. The paper presents an internal classification of the group and briefly discusses the external relationships of these languages. On the basis of existing data, a review of the basic phonology and noun class prefix systems is given.
Handwritten Font Image Design System Based on Deep Learning Algorithm

Deep learning technology is an important sub-discipline in the field of machine learning technology today, and the predecessor of its development is neural networks. In recent years, due to huge breakthroughs in information technology, deep learning has achieved good development results in computer vision, natural language processing, machine translation, unmanned driving, and artificial intelligence technology closely related to robotics. Like other deep learning fields, it has to face the two problems of overfitting and computational efficiency. This article aims to study a handwritten font image design system based on deep learning algorithms. Based on the analysis of factors affecting recognition rate, the concept of deep learning, the classification of deep learning algorithms, and the classification of recognition results, a handwriting based on convolutional neural network is constructed. The font recognition network model is trained on the built model through the MNIST data set, and the model is subjected to standardized recognition tests on the test set created by me. Experimental test results show that the system design basically meets the practical requirements of handwritten font recognition.
Methodological Features of the Formation of Ideas about Units of Time Measurement in Primary Schoolchildren (Correcting All-Russian Verification Work Errors)

A very important skill that helps younger schoolchildren to navigate in the world around them is the ability to determine the time with the help of a clock. The development of temporal representations in them is of vital practical importance. How to make the process of formation and development of temporal representations attractive for younger schoolchildren? The article provides methodological recommendations, as well as a number of simple, exciting tasks that allow you to create a positive atmosphere in the lesson and stimulate students' interest in studying the category of time.
Seasonality Atlas of Solar Radiation in Mexico

Due to the imminent climate-change emergency, it is urgent to boost the exploitation of renewable resources to produce clean energy, being solar energy one of the most promising ones. However, one of the greatest challenges that solar energy faces is its intermittency. Thus, to get the biggest benefit from this resource, especially for photovoltaic generation, it is required to predict its availability to estimate variations in energy production. As the first step for solar radiation forecasting, a seasonality analysis is mandatory to obtain better results. In this work, we perform a seasonality analysis of solar radiation in Mexico using Machine Learning. Specifically, we accomplish a cluster analysis of solar radiation data in locations representative of the different climate conditions in Mexico to obtain a seasonality atlas of the solar resource. Cluster analysis is performed with two algorithms, k-means and k-medoids. Finally, the Silhouette method is used to validate the results.
Clustered Problems and Machine Learning Methodologies: A New Approach

The future of production is smart and autonomous and so should the technologies that allow it. The application of machine learning (ML) in the manufacturing sector has been designated as one of the key players in the digital transformation. In general, the tendency of most ML application fields goes into more complex algorithms and deeper architectures. However, it is not the only available approach and often times not the most suitable for all use cases in the manufacturing industry where environments and products are highly standard. ML has the potential to solve complex problems by finding and learning relationships that humans cannot identify. Our hypothesis is that these relationships are invariant and exist inside specific tasks but also between a wide catalogue of similar, although not yet known, problems. An algorithm that finds and solves these invariances at a general and abstract level creates a general solution that can be directly deployed into new use cases leading to an increase in efficiency by reducing the required effort, time, and knowledge and allowing to leverage ML. Through three preliminary use cases, we demonstrate the potential of applying ML at a general and abstract level to increase the flexibility and reduce the model, task, and dataset dependency of ML solutions. This approach facilitates the conversion of automated environments into autonomous environments.
Multi-Scale Feature Fusion Network for Video-Based Person Re-Identification

In recent years, person re-identification technology has been greatly developed. Image-based person re-identification algorithms have achieved excellent performance on open source datasets. In contrast, the development of video-based person re-identification technology is relatively backward. At present, the main research work of video-based person re-identification algorithms is focused on the processing of temporal information in the picture sequence. Complex appearance features are not effective when performing temporal fusion, so the frame-level features used are almost based on global features. This paper proposes a video person re-identification model based on multi-scale feature fusion. The multi-scale feature fusion of the model is embodied in the design of the frame-level feature extraction module. This module extracts the frame-level features of different scales, and then catenates them together into vectors, which not only improves the feature discrimination degree, but also makes the catenated frame-level features carry out effective temporal fusion, and the test results on the Mars dataset have reached a competitive level. At the same time, a series of comparative experiments were carried out on the model parameters to achieve further optimization of performance.
Exploiting Pre-trained Encoder with Receptive Fields and Squeeze-Excitation module for Road Segmentation

Autonomous vehicles will decrease the number of accidents on the road caused by human error. Intelligent vehicles have traditionally advanced in a step-by-step manner. These developments boost the automation scene in vehicles by incorporating systems that facilitate the driver in maintaining a constant speed, adhering to a lane, or transferring control over vehicle and driver. Autonomous vehicles must have a thorough understanding of their surroundings. As a result, object detection and road scene segmentation are critical in navigation for recognizing the drivable and non-drivable areas. Towards the development of the completely automated framework for road scene segmentation, we propose an RFB-SELinkNet that utilizes the SEResNeXt model as a feature extractor and receptive field block (RFB) with squeeze and excitation (SE) module for better feature representations. Our proposed framework outperforms D-LinkNet, Eff-UNet, and other state-of-art models. According to the experiments, the proposed model achieves 0.698 mloU and produces good segmentation outcomes on the validation set of the India Driving Lite (IDD Lite) dataset.
A Multi-branch Ensemble Agent Network for Multi-agent Reinforcement Learning

Multi-agent Reinforcement Learning (MARL) has drawn wide attention in recent years as a bunch of real-world complex scenes can be abstracted as multi-agent systems (MAS). Partially observable cooperative multi-agent setting, in which agents have to learn to coordinate with allies by actions conditioning on their own partial observation and share a single global reward each time-step, is the most concerned MAS by existing MARL algorithms with centralized training and decentralized executing. One key challenge is how to make effective oriented exploration. In this work, we propose a new agent network called Multi-branch Ensemble Agent Network (MEAN) to encourage the oriented exploration. We evaluate our MEAN with existing Q-learning based MARL algorithms on StarCraft II micro-management challenges. Extensive evaluations show that algorithms equipped with MEAN achieve much better performance on both homogeneous and heterogeneous scenarios compared with the initial algorithms.
GPU Parallelization of All-Pairs-Shortest-Path Algorithm in Low-Degree Unweighted Regular Graph

The design of the network topology of a large-scale parallel computer system can be represented as an order/degree problem in the graph theory. To solve the order/degree problem, we have to obtain all-pairs-shortest-path (APSP) for the graph. A conventional APSP algorithm for GPUs is based on the adjacency matrix (ADJ-APSP). When focusing on low-degree and unweighted graphs, most of the matrix elements are zero in the first few iterations of the algorithm. We will further speed up the APSP algorithm by treating the adjacency matrix as a sparse matrix in the first iterations of the algorithm. Evaluation results show that our proposed algorithm on a single GPU (NVIDIA GeForce RTX 3080) reduces the execution time by up to 32.7 % compared to the conventional algorithm.
Document Image Binarization Using Visibility Detection and Point Cloud Segmentation

In order to solve the problems of degradation, uneven illumination, and shadows in the process of binarization of document images, two approaches were proposed. In this paper, many state of the art were deeply studied and an improved scheme were proposed. According to position and intensity information of each pixel in the document image, the image was mapped into 3D space. The visibility detection technology of 3D point cloud combined with convex hull calculation was used to extract background of the image and enhance the input image of any binarization algorithm. Based on this, the authors proposed another new binarization algorithm to remove shadows, in which does not need special illumination uniformity solution or shadow removal algorithm. The algorithm can generate binary images in one step. Document images with shadows were mapped into 3D space, the characteristic of the point cloud was distinctly hierarchical. The idea used its hierarchy in 3D space to calculate the set of points at convex hull several times, then selected the non-text points and segmented them from the text, and then obtained the binary image. The experimental results demonstrated the effectiveness of the two algorithms.
A Personalized Recommender System Using Real-Time Search Data Integrated with Historical Data

With companies focusing intensively on customer experience, personalization and platform usability have become crucial for a company's success. Hence, providing appropriate recommendations to users is a challenging problem in various industries. We work toward enhancing the recommendation system of a timeshare exchange platform by leveraging real-time search data. Previously, the recommendation model utilized historical data to recommend resorts to users and was deployed online once a day. The limitation of this model was that it did not consider the real-time searches of the user, hence losing context. This directly impacted the click-through rate of the recommendations, and the users had to navigate the website excessively to find a satisfactory resort. We build a model such that it utilizes not only the historical transactional and master data but also the real-time search data to provide multiple relevant resort recommendations within 5 s.
Benchmarking Performance of Document Level Classification and Topic Modeling

Text classification of low resource language is always a trivial and challenging problem.This paper discusses the process of Urdu news classification and Urdu documents similarity.Urdu is one of the most famous spoken languages in Asia.The implementation of computational methodologies for text classification has increased over time.However, Urdu language has not much experimented with research, it does not have readily available datasets, which turn out to be the primary reason behind limited research and applying the latest methodologies to the Urdu.To overcome these obstacles, a mediumsized dataset having six categories is collected from authentic Pakistani news sources.Urdu is a rich but complex language.Text processing can be challenging for Urdu due to its complex features as compared to other languages.Term frequency-inverse document frequency (TFIDF) based term weighting scheme for extracting features, chi-2 for selecting essential features, and Linear discriminant analysis (LDA) for dimensionality reduction have been used.TFIDF matrix and cosine similarity measure have been used to identify similar documents in a collection and find the semantic meaning of words in a document FastText model has been applied.The training-test split evaluation methodology is used for this experimentation, which includes 70% for training data and 30% for testing data.State-of-the-art machine learning and deep dense neural network approaches for Urdu news classification have been used.Finally, we trained Multinomial Naïve Bayes, XGBoost, Bagging, and Deep dense neural network.Bagging and deep dense neural network outperformed the other algorithms.The experimental results show that deep dense achieves 92.0% mean f1 score, and Bagging 95.0% f1 score.
Ubiquitous Memory: A field study on ubiquitous technology systems and interaction design for the symbolic appropriation of spaces through collective memory

This paper presents the results of a case study on how ubiquitous technology can trigger symbolic appropriation dynamics, or placemaking from a community, in the context of building abandonment in the historic downtown. We focus our approach on the creation of an interdisciplinary methodology between communication strategies, information design and interaction systems which converged in the generation of a public interactive display which can mediate sensorial and symbolically appreciations for different audiences through play and interaction. We focus our approach on the creation of an interdisciplinary methodology including communication strategies, information design and interaction systems which converged in the generation of a public interactive display which can mediate sensorial and symbolical appreciations for different audiences through the use of play and interaction.
Designing an Intelligent Agents for E-Bookstore System Web-Based System

Artificial Intelligence (AI) is a new field triggered in computer science in order to solve complex problems like cooperation, distribution, and communication. AI has many research areas for the development of complex systems. Multi-Agent Systems (MAS) are one of reach of using autonomous systems which have actors’ acts on their own based on user requirements to solve their problems. A new development of programming language called Agent-Oriented Programming (AOP) for programming autonomous agents and multi-agent system MAS. The agent in MAS acts in an autonomous way without any human or system interaction. The agent has the ability of automated action in order to reach its goal. An agent has the Belief-Desire-Intension (BDI) architecture in order to define the behavior of the system using Agent-Oriented Programming. This paper shows a proposed model which is an intelligent multi-agent system for e-bookstore that communicates with each other using a Foundation of Intelligent Physical Agent (FIPA). Also this paper shows the search performance within the communication results worked on Amazon Dataset and evaluates the quality of intelligent agents of E-bookstore system.
PDPM: A Patient-Defined Data Privacy Management with Nudge Theory in Decentralized E-Health Environments

A private decentralized e-health environment, empowered by blockchain technology, grants authorized healthcare entities to legitimately access the patient's medical data without relying on a centralized node. Every activity from authorized entities is recorded immutably in the blockchain transactions. In terms of privacy, the e-health system preserves a default privacy option as an initial state for every patient since the patients may frequently customize their medical data over time for several purposes. Moreover, adjustments in the patient's privacy contexts are often solely from the patient's initiative without any doctor or stakeholders' recommendation. Therefore, we design, implement, and evaluate user-defined data privacy utilizing nudge theory for decentralized e-health systems named PDPM to tackle these issues. Patients can determine the privacy of their medical records to be closed to certain parties. Data privacy management is dynamic, which can be executed on the blockchain via the smart contract feature. Tamper-proof user-defined data privacy can resolve the dispute between the e-health entities related to privacy management and adjustments. In short, the authorized entities cannot deny any changes since every activity is recorded in the ledgers. Meanwhile, the nudge theory technique supports providing the best patient privacy recommendations based on their behaviour activities even though the final decision rests on the patient. Finally, we demonstrate how to use PDPM to realize user-defined data privacy management in decentralized e-health environments.
A New Matrix Model for Human-AI Integration

This paper has the goal to describe the introduction of an AGI (Artificial General Intelligence) in a human organization. An organization may be composed of project leaders (vertical communication), human and non-human operations, an AGI machine that manages horizontal communication and big data. This leads to the paradigm of the Organizational Great Machine (GM) in which humans benefit from upgrade or empowerment provided by an all-encompassing artificial intelligence. An AGI has a substrate that uses Multi Agent System (MAS) where the model of an agent is defined as a belief, desire, intention (BDI) model of an agent augmented with Situations and Action (SBDIA). This provides a way of explaining future-directed intention. SBDIA has a natural model consistent with human psychology. Once artificial intelligence is introduced into the organization, we have to design new Human-Machine Integration (HMI) tools to have a successful relationship with human resources. At the end the organizational model of the MAS, called Great machine (GM), is used to provide information and transparency about the behavior of an AGI. A GM's internal model is considered as a knowledge market where knowledge flows among agents. Leontief's economic model emulates this market by providing information on how knowledge flows and how it is used. This permits more transparency and an understanding of internal processes.
Primary Applications for Geosciences

After introducing the principles of remote sensing for hydrocarbon exploration, the data provided by remote sensing sensors and methods for processing the data, we will now turn to the primary applications to geosciences including rendering and interpreting remote sensing data. We will study how the earth surface texture is related to the properties and configuration of the subsurface rock layers using the concept of geomorphology. In this section we show how the rendering of remote sensing data can reveal properties of individual remote data sets as well as the correlation between multiple data sets. Equipped with the data, the processing and the rendering, we will then study three geological settings that are important for various aspects of exploration. We start with the mapping of structural features, i.e. folds and faults, and then study depositional and erosional features in arctic and hot desert environment using both direct methods mapping features at the earth surface as well as using indirect methods to infer geomorphologic properties from observations of vegetation and water features.
Quad-rotor UAV Audio Recognition Based on Mel Spectrum with Binaural Representation and CNN

With the wide application of UAV in civil and military fields, more and more attention has been paid to the accurate detection for UAV. In this paper, the noise of UAV is taken as the research object, and the audio recognition technology based on Mel spectrum with binaural representation and CNN is used for multi-UAV recognition. Firstly, the noise of UAV, bird chirp, traffic noise and other environmental noises are collected, and the audio dataset of UAV audio is made after preprocessing. Secondly, the Mel spectrum with binaural representation is proposed for audio feature extracting, and one-dimensional audio is converted to four channel spectrum with rich feature. Finally, designed deep CNN is trained by spectrum map for UAV audio recognition. Experimental results show that the proposed method is superior and the average accuracy is 99.24%.