Reviewerly/exHarmony huggingface dataset:
Identifying Drone Web Sites in Multiple Countries and Languages with a Single Model

A text-based, bag-of-words, model was developed to identify drone company websites for multiple European countries in different languages. A collection of Spanish drone and non-drone websites was used for initial model development. Various classification methods were compared. Supervised logistic regression (L2-norm) performed best with an accuracy of 87% on the unseen test set. The accuracy of the later model improved to 88% when it was trained on texts in which all Spanish words were translated into English. Retraining the model on texts in which all typical Spanish words, such as names of cities and regions, and words indicative for specific periods in time, such as the months of the year and days of the week, were removed did not affect the overall performance of the model and made it more generally applicable. Applying the cleaned, completely English word-based, model to a collection of Irish and Italian drone and non-drone websites revealed, after manual inspection, that it was able to detect drone websites in those countries with an accuracy of 82 and 86%, respectively. The classification of Italian texts required the creation of a translation list in which all 1560 English word-based features in the model were translated to their Italian analogs. Because the model had a very high recall, 93, 100, and 97% on Spanish, Irish and Italian drone websites respectively, it was particularly well suited to select potential drone websites in large collections of websites.
A TABOO-NOT in OPEN World Assumption for A Natural Language based Logic Programming

Traditional logic programs are written in some formal languages easy to “unify” each other or symbol based ones such as in Prolog, and their “NOT” mechanisms are “negation by failure of proof” according to “CLOSED world assumption” and are not assumed as “true negation”s.On the other hand, we suppose that BigData for moral, ethics and values are written originally in various different natural languages over the world.In order for both human and AI to easily understand and automatically operate such BigData, we hope that (1) human could logic-program the contents of such Big-Data, by semi-dead-copying the original-natural-language-texts as "logic-programs" without hard translation, formalization and/or normalization,(2) “NOT”s in the language text that mean some TABOOs in any culture shall be operated correctly as such (not as “negataion by failure of proof”s but as the TABOOs) even in case of concretizing objects from different cultures, as in OPEN world assumption, because any TABOO from any one culture shall be rigidly respected, even during meetings between people from different cultures including the one culture with the TABOO.For the above purpose, this document explains a method of natural-language-based-logic-programming where variables for knowledge abstraction and concretization are embedded into the natural language texts, and also explains the above strong "TABOO-NOT" implementation therefor.
ConShuffleNet: An Efficient Convolutional Neural Network Based on ShuffleNetV2

CNNs have shown powerful capabilities for classification tasks. However, this powerful capability takes up huge computation budget and storage space, it is a major challenge to achieve high accuracy with limited computation resources and storage. We propose an efficient convolutional neural network architecture based on ShufflNetV2 called ConShuffleNet. To be specific , we use a pyramid dilated depthwise convolution (PDDWConv) which consists of $$3\times 3$$ depthwise convolution and $$3\times 3$$ dilated depthwise convolution (DDWConv) with a dilation rate of 2 instead of $$3\times 3$$ depthwise convolution (DWConv) of ShufflNetV2 to capture multi-scale spatial information, and integrate the Coordinate Attention (CA) module to enable the model to obtain more important features. We use the CIFAR-100 benchmark dataset as our experimental dataset. Experimental results show that the proposed method can achieve better accuracy with similar model size and computational complexity level.
Research on Cluttered Object Recognition Algorithm Based on Improved YOLO V5

Aiming at the problems of slow speed and low accuracy in the automatic recognition of traditional robot workpieces, this paper proposes an improved workpiece recognition method based on YOLO V5. First, this paper collects photos of common workpieces by camera, creates data set independently and performs data enhancement to fill the gap of workpiece data set. Since YOLO V5 has the problems of low recognition accuracy and missed detection for small target objects, the feature extraction capability of the network is enhanced by incorporating an attention module to its backbone network. Secondly, the SIoU loss function is used to speed up the convergence speed and improve the regression accuracy, which enhances the network accuracy. Finally, the trained model is compared and evaluated with the original model. The test results show that the improved YOLO V5 target detection algorithm improves 4.58% and 3.40% in accuracy and recall, respectively, compared with the original algorithm, which can effectively avoid the phenomena of missed detection, false detection and overlap, and has significant advantages over existing methods and is more suitable for the detection needs in practical production applications.
Measuring the Importance of Smart E-learning education system

Advances in information technology facilitate new (or improved) educational and training practices, creating opportunities for new methods and tools; while also, these technologies are changing the educational paradigm. In the past, smart education frameworks were mostly qualitative studies, describing the conceptual framework (and its connotations) of smart educational systems. Although these conceptually structured smart education systems can promote student learning, peer and teacher interaction, and teaching practice, which assist in understanding students’ status and needs in multiple ways and providing students with real-time synchronous/asynchronous guidance and help, unfortunately, many qualitative studies in the past still lack quantitative data to verify the priority of E-Education planning. The experts surveyed by AHP in this article are selected from E-learning and information technology, with a total of 14 people. The results from the software, Expert Choice, found that in the planning of the smart education system, the importance order of the main criteria, from the most important to the least were smart classroom function, technology-based learning system (IoT, Metaverse), teaching monitoring system, conceptual elements of smart learning, respectively. The sub-criteria weight values showed the key factors of E-Learning Education Planning, which in order were educational resources optimization, teaching is differentiated from person to person, students’ cooperation learning, course completion rate, reliable wireless connection (Wi-Fi, IoT apps, Wearable technology). Finally, the least important were smart pedagogies based on learning theory, learning attention detection, and system data backup. In terms of contribution, to our knowledge, very few studies have used the AHP model and hierarchical design method presenting in the current research to quantitatively demonstrate smart education planning and lecture design, to identify and verify the feasibility model of smart education.
Quantifying syntax similarity with a polynomial representation of dependency trees

We introduce a graph polynomial that distinguishes tree structures to represent dependency grammar and a measure based on the polynomial representation to quantify syntax similarity. The polynomial encodes accurate and comprehensive information about the dependency structure and dependency relations of words in a sentence, which enables in-depth analysis of dependency trees with data analysis tools. We apply the polynomial-based methods to analyze sentences in the ParallelUniversal Dependencies treebanks. Specifically, we compare the syntax of sentences and their translations in different languages, and we perform a syntactic typology study of available languages in the Parallel Universal Dependencies treebanks. We also demonstrate and discuss the potential of the methods in measuring syntax diversity of corpora.
Learning Enhancement Using Question-Answer Generation for e-Book Using Contrastive Fine-Tuned T5

The rise of E-learning systems offers a vast amount of free educational content for every inquisitive e-learner. However, reading only e-content does not makes their learning effective. Posing appropriate questions during the reading process can aid in the learner’s comprehension. We present a novel approach to create educational Question-Answers on eBook content. The model first summarizes key information from an input document, which is then used for creating relevant Question-Answers (QAs). We build our educational Question-Answers generation model by fine-tuning a pretrained LM (T5) using maximum likelihood estimation. We also present a contrastive fine-tuning method, in which the contrastive loss is added between the positive and negative training feature pairs during the fine-tuning process. The extensive evaluation methods on QA dataset (FairytaleQA and HotPotQA) and NCERT e-book, demonstrate the effectiveness and practicability of our eQA model.
Research on the Application of AI Artificial Intelligence Technology in Intelligent Buildings

This study aims to reveal the application of artificial intelligence, and determine the opportunities and challenges of artificial intelligence application in the construction industry, and proposes the design of applying artificial intelligence technology, which provides a research basis for future smart city design and development.Additionally, AI applications in construction are pointed to in this study, and the study provides insights into key applications of AI for construction-specific challenges.
Outranking Relations based Multi-criteria Recommender System for Analysis of Health Risk using Multi-objective Feature Selection Approach

The recommender system filters out important information from a large pool of information to set some important decisions in terms of recommendation. It has had an impact in almost every domain, including health, where it can extract information from massive amounts of digital data on patients to provide a better understanding of their clinical status and predict diseases. It is found that most of the real-world problems are dealing with multiple (conflicting) criteria, and handling those criteria in decision making is a challenging task too. In this context, this paper has proposed a health recommender system based on multi-criteria ranking and a multi-objective feature selection approach. Certain risk factors for the development of cervical cancer in women have been recommended by the proposed system, as well as some models for reliable prediction of this disease. The performance of the proposed system is tested and analysed by implementing it on the Cervical Risk Classification dataset with the three different ranking algorithms of Multi-Criteria Decision (MCDM) Making. It is found that the MOORA (Multi-objective Optimisation on the Basis of Ratio Analysis) MCDM algorithm outperforms the other two algorithms in terms of [email protected], [email protected], [email protected] and Mean Reciprocal Rank (MRR)@N. The performance of the proposed system is also verified using four other benchmarking disease datasets, and statistical Anova and Wilcoxon tests have been done to validate the results.
Automatic Classification of Tumor Response From Radiology Reports With Rule-Based Natural Language Processing Integrated Into the Clinical Oncology Workflow

Imaging reports in oncology provide critical information about the disease evolution that should be timely shared to tailor the clinical decision making and care coordination of patients with advanced cancer. However, tumor response stays unstructured in free-text and underexploited. Natural language processing (NLP) methods can help provide this critical information into the electronic health records (EHR) in real time to assist health care workers.A rule-based algorithm was developed using SAS tools to automatically extract and categorize tumor response within progression or no progression categories. 2,970 magnetic resonance imaging, computed tomography scan, and positron emission tomography French reports were extracted from the EHR of a large comprehensive cancer center to build a 2,637-document training set and a 603-document validation set. The model was also tested on 189 imaging reports from 46 different radiology centers. A tumor dashboard was created in the EHR using the Timeline tool of the vis.js javascript library.An NLP methodology was applied to create an ontology of radiographic terms defining tumor response, mapping text to five main concepts, and application decision rules on the basis of clinical practice RECIST guidelines. The model achieved an overall accuracy of 0.88 (ranging from 0.87 to 0.94), with similar performance on both progression and no progression classification. The overall accuracy was 0.82 on reports from different radiology centers. Data were visualized and organized in a dynamic tumor response timeline. This tool was deployed successfully at our institution both retrospectively and prospectively as part of an automatic pipeline to screen reports and classify tumor response in real time for all metastatic patients.Our approach provides an NLP-based framework to structure and classify tumor response from the EHR and integrate tumor response classification into the clinical oncology workflow.
StaC: Stacked chaining for multi-label classification

Multi-label classification has a fundamental challenge of finding the interdependence of labels. Stacking is one of the most prominent methods for the same. It uses the full dependency, i.e., it utilizes dependence among all the labels. The stacking method uses classifiers in two layers, where the first layer’s classifiers assume no interdependence among labels. It leads to the first layer classifiers being error-prone. Also, it diminishes the performance of the second layer’s classifiers because they are fed with the first layer’s classifiers’ outputs. Further, there is no correlation utilization among the second layer’s classifiers. This paper addresses the poor performance of the first layer’s classifiers and no correlation utilization at the second layer. It proposes a method called Stacked Chaining (StaC) that uses interdependence of labels in a chaining fashion at both layers. This way, it resolves the poor performance of the first layer and also incorporates label correlation at the second layer. The StaC method is compared with other prevalent state-of-the-art methods, including Classifier Chain and Stacking, using ten benchmark multi-label datasets. Experimental results show that the proposed StaC method achieves better performance than other state-of-the-art methods for different performance evaluation parameters.
Revealing the orogenic genesis of Huogeqi Cu-Pb-Zn deposit: Evidence from the machine learning-based data interpretation of pyrite geochemistry

Orogenic-type gold deposit is of great economic importance worldwide and its ore-fluid is believed to have poor ability in transporting base metals such as Cu, Pb, and Zn. However, increasing numbers of case studies have shown that base metal deposits can also form in similar geological processes as orogenic gold, and share similar characteristics with the latter. This leaves whether orogenic-type deposit is gold-only an open question for economic geologists. The shear zone-controlled Huogeqi Cu-Pb-Zn deposit is located in the north margin of the North China Craton. Although it is still debated whether this deposit is orogenic-type or of syngenetic origin, our previous geological, fluid inclusion, geochronological and stable isotopic studies have shown that it shares broad similarities with orogenic-type deposit. In this study, an alternative approach, the geochemistry of syn-ore pyrite coupled with supervised machine learning-based data interpretation, is employed to discriminate the genesis of this deposit. It is shown that the syn-ore pyrite is geochemically similar to those from orogenic-type deposits rather than VMS or sedimentary pyrite, and thus confirms the orogenic genesis and unambiguously exclude the syngenetic origin of this deposit from a different perspective. The results in this study support the view that orogenic-type ore-forming system can also form base metal deposit, which is consistent with the studies and simulation results by other researchers. In addition, since the pyrite can only record the geochemistry information in the final deposition of the mineral and eliminate the histories before the last deposition stage, the data interpretation of pyrite geochemistry by machine learning method is a powerful tool to discriminate the genesis of deposit, especially suitable for the deposits with multistage ore-forming processes.
Pre-Historic Intra-continental Africa-India Proximity Series on VedicPeriod-Earth: Homogenizing Values Amidst Heterogeneous Variables Bon a Spencer

The expression "bon à penser", is widely used in several University Departments of Social Sciences with the most common English translation in the academic literature meaning that which is "good to think with".This paper is specific on African-Indian basketry of the heterogeneous variables managed by homogenizing values.Hence bon a spencer!Bearing in mind that it is the Vedic-period-earth for the proximity of Africa-India's prevalence over the conundrum of heterogeneous variables throughout the triad of peoples, languages and kingdoms on one hand, while overshadowing the other, the next shadow was of inevitable homogenization of existing values, processes and integrals in terms of sociological institutions, systems, methods, practices and harmonies.Yet nuances did indeed have a chance to sprout with seeds sprouting time after time from the causes and effects between the enjoined heterogeneous differences amidst nutritious and meritorious homogeneous processes like a beehive of homogeneity that outweighed heterogeneity.The Vedic geo-homogeneity-cum-geo-Vedic-heterogeneity's bi-tri-quartic dimensionalities do matter for super official research, if not superficial.Forensics like geo-Vedic homogeneity of geo-graphic heterogeneity, Vedic holy trinity's homogeneity-in-heterogeneity, Vedic syncretism & hieroglyphs' homogeneity-in-heterogeneity and Vedic-geo-homogeneity-cum-geo-Vedic-heterogeneity as diverse array of Africa-India proximity bon a spencer.
Image downscaling via co-occurrence learning

Image downscaling is one of the widely used operations in image processing and computer graphics. It was recently demonstrated in the literature that kernel-based convolutional filters could be modified to develop efficient image downscaling algorithms. In this work, we present a new downscaling technique which is based on kernel-based image filtering concept. We propose to use pairwise co-occurrence similarity of the pixelpairs as the range kernel similarity in the filtering operation. The co-occurrence of the pixel-pair is learned directly from the input image. This co-occurrence learning is performed in a neighborhood based fashion all over the image. The proposed method can preserve the high-frequency structures, which were present in the input image, into the downscaled image. The idea is further extended to the case of fractions factor of downscaling. The resulting images retain visually-important details and do not suffer from edge-blurring artifact. We demonstrate the effectiveness of our proposed approach with extensive experiments on a large number of images downscaled with various downscaling factors.
MaRF: Representing Mars as Neural Radiance Fields

The aim of this work is to introduce MaRF, a novel framework able to synthesize the Martian environment using several collections of images from rover cameras. The idea is to generate a 3D scene of Mars’ surface to address key challenges in planetary surface exploration such as: planetary geology, simulated navigation and shape analysis. Although there exist different methods to enable a 3D reconstruction of Mars’ surface, they rely on classical computer graphics techniques that incur high amounts of computational resources during the reconstruction process, and have limitations with generalizing reconstructions to unseen scenes and adapting to new images coming from rover cameras. The proposed framework solves the aforementioned limitations by exploiting Neural Radiance Fields (NeRFs), a method that synthesize complex scenes by optimizing a continuous volumetric scene function using a sparse set of images. To speed up the learning process, we replaced the sparse set of rover images with their neural graphics primitives (NGPs), a set of vectors of fixed length that are learned to preserve the information of the original images in a significantly smaller size. In the experimental section, we demonstrate the environments created from actual Mars datasets captured by Curiosity rover, Perseverance rover and Ingenuity helicopter, all of which are available on the Planetary Data System (PDS).
Sign Language Recognition Using Machine Learning

Communication is vital for humans. It helps to share knowledge and information and develop relations with others. We know that it is hard for people to communicate with people speaking sign language. Therefore, the purpose of this work is to implement a machine learning model for classifying and identifying hand gestures like sign language for translating interactions into written and oral forms. We have developed a machine learning model which will detect hand gestures and translate it to words which will help people to understand sign language.
A Multilayer Encryption Model to Protect Healthcare Data in Cloud Environment

21st Century is recognized as an era of cloud computing and it has become an integral part for any organization. It is equally suitable for all the organizations e.g. education, government, public sector, health care department There are two types of patient information i.e. protected/sensitive health information and general information. Protected information (Phone no, ATM, Security no, MR no etc.) requires more confidentiality as compared to general information. Therefore, for some protected health information without patient association (general disease name, symptoms) will be very helpful for research experiments. Health information is protected by achieving confidentiality, integrity and availability, when data is stored in cloud environment. Cryptography methods provide different techniques to protect the data stored in cloud environment. In this paper, we have suggested a multilayer encryption technique to ensure the confidentiality of data stored in cloud environment. This suggested technique will improve the security of cryptographic techniques when used in multilayered format. We have set up a local system for the experiment. We have used the RDBMS (Microsoft SQL Server) and Framework 4.5. A set of 500 dummy patient records is used to test the proposed techniques. The experiment was performed to check the confidentiality of the suggested techniques. This experiment shows us that multilayer encryption techniques is more suitable for public health sectors when data is in cloud environment.
Toward Reliable Software Analytics: Systematic Integration of Explanations From Different Model-Agnostic Techniques

This article discusses the problems caused by the inconsistent explanations generated from different model-agnostic techniques for software analytics. We propose a method to integrate inconsistent explanations to derive information that can provide more useful and reliable software analytics to practitioners.