Reviewerly/exHarmony huggingface dataset:
Floor Area Detection by Multiple-classifier Method Based on Improved Fuzzy Integral

FI), artificial bee colony (ABC), fully convolutional network (FCN), area contour detection (ACD).The areas of application of mobile robots are becoming increasingly extensive.Most mobile robots use visual images to obtain information about the surrounding environment, including information about the floor area that can be traversed or obstacles to be avoided.In this study, we propose a multiple-classifier method based on an improved fuzzy integral (IFI) for floor area detection using charge-coupled device sensors.The multiple classifiers include deep learning networks (DLNs) and an area contour detection (ACD) algorithm.The IFI in the proposed method is different from the traditional fuzzy integral in that its fuzzy density value is optimized using the artificial bee colony algorithm.The proposed method takes the outputs of several classifiers and the ACD algorithm as the input.The IFI is used to evaluate each classifier to leverage its advantages for the proposed method.Finally, the classification results are calculated to achieve accurate floor area detection.To verify the performance of the proposed method, we used the public MIT scene dataset and images of various indoor environments as the study dataset, with 200, 48, and 48 images used as training data, verification data, and test data, respectively.The experimental results revealed that the DLN-ACD-IFI combination achieved an average floor area detection accuracy of 97.8%, thus improving the overall recognition rate.
Construction of College Physical Education MOOCS Teaching Model Based on Fuzzy Decision Tree Algorithm

With the continuous development of the MOOCS model in college physical education, the corresponding teaching evaluation has also been widely concerned by the community. The development of a traditional education mode in college physical education cannot meet the current teaching requirements. In order to solve the problem of narrow application and insufficient accuracy in traditional education, on the basis of the Kohonen fuzzy decision tree algorithm and the MOOCS fuzzy decision tree algorithm, a fuzzy evaluation model of P.E. teaching is proposed. The results show that the fuzzy ID3 algorithm can achieve high accuracy in the four databases, and the classification accuracy of the A-D database is 75.9%, 62.9%, 76.6%, and 95.1%, respectively. Except for database C, the classification accuracy of the minimum classification uncertainty algorithm is lower than the other two clear decision tree algorithms. Compared with the minimum classification uncertainty algorithm, the fuzzy ID3 algorithm has obvious advantages in classification rules. The classification rules of A-D database are 18, 12, 16, and 10, respectively. When the authenticity threshold is about 0.8, the fuzzy ID3 algorithm has the highest classification accuracy. The proposed MOOCS model for college physical education based on the fuzzy decision tree algorithm has strong practicability and high accuracy. This paper studies the MOOCS model of college physical education and introduces the fuzzy decision tree algorithm to evaluate the MOOCS model of college physical education. It solves the problem that traditional sports cannot satisfy the needs of “Internet plus education” at all. Compared with the traditional sports model, it has better applicability and higher accuracy.
Research on the Current Situation and Strategy of Take-out Garbage Disposal in College Campuses under the Concept of Green Development

With the development of the Internet, take-out ordering has become the first choice for more and more college students, but the current take-out garbage mixed with domestic garbage greatly hinders the green disposal of garbage. As a gathering place for cultivating talents, colleges and universities should become the forerunners in the reasonable disposal of take-out garbage. Through questionnaires, on-the-spot investigations, etc., this project conducts in-depth research on the reasons why college students order takeaways, how to deal with take-out garbage, and the problems existing in the classification of take-out garbage in colleges and universities, and puts forward sustainable and green improvement suggestions in order to change The current situation of take-out garbage disposal on campus reduces the burden of urban garbage disposal and provides a new way for major universities to deal with take-out garbage.
An Effective Initialization Method Based on Quartiles for the K-means Algorithm

Objectives: This study aims to speed up the K-means algorithm by offering a deterministic quartile-based seeding strategy for initializing preliminary cluster centers for the K-means algorithm, enabling it to efficiently build high-quality clusters.Methods: We have investigated various cluster center initialization approaches in literature and presented our findings.For the Kmeans algorithm, we here propose a novel deterministic technique based on quartiles for finding initial cluster centers.To obtain the preliminary cluster centers, we have applied our suggested approach to the data set.The initial cluster centers determined by our suggested method are then entered into the K-means algorithm.The proposed seeding method is evaluated on sixteen benchmark clustering data sets: five synthetic and eleven real data sets.Python is used to run the simulation.Findings: Based on empirical results from experiments, it is evident that our proposed cluster center initialization method allows the K-means algorithm to form clusters with SSE values comparable to the minimum SSE values produced by repeated Random or K-means++ initializations.Furthermore, our deterministic initialization strategy assures that the K-means algorithm converges faster than the Random and K-means++ initialization techniques.Novelty: In this study, we explore the potential of quartile-based seeding as a technique of accelerating the Kmeans algorithm.Needless to add, as our seeding method is deterministic, the requirement to run K-means repeatedly with different stochastic initializations is completely eliminated.Also, our initialization strategy assures that there is remarkable saving in execution time as compared to the Random and K-means++ initialization techniques.Moreover, it is found that after initializing with our offered method, the solution obtained with just a single run of K-means produces optimal clusters.Applications: Our proposed seeding technique will be helpful for initializing the K-means algorithm in time-sensitive applications, applications managing large amounts of data, and applications looking for deterministic cluster solutions.
PERBANDINGAN ALGORITMA NAÏVE BAYES, SVM DAN XGBOOST DALAM KLASIFIKASI TEKS SENTIMEN MASYARAKAT TERHADAP PRODUK LOKAL DI INDONESIA

Marketplace has become a popular online transaction medium with various features taken, one of the features that can be used for research is online reviews. Reviews can also be used as a data source for making various management decisions. Online reviews are very important in supporting purchasing decisions because of the development of e-commerce, there are more and more fake reviews so that more consumers are worried about online shopping. This cannot be denied because customer reviews can determine the level of customer satisfaction with the products that have been purchased. Sentiment analysis can be applied to Marketplace product reviews so that it can be used as product improvement suggestions for sellers and competitors so that they can find out what products are pleasing and needed by the community. Based on research that has been done, that the combination of Word2vec + XGBoost produces a higher F1 score of 0.941 followed by TF-IDF + XGBoost 0.940. Meanwhile, the SVM algorithm using vector space TF-IDF and Word2vec only produces 0.938 and 0.939. Meanwhile, Naïve Bayes has an F1-Score of 0.915 with TF-IDF and 0.900 with word2vec. Classification with word2vec in representing words into vectors is better than TF-IDF, this is because the advantages of word2vec are able to process semantic relations between words.
Modeling and Analysis of the Influence of Cultural Differences on English Learning from the Perspective of the Cultural Community

A model of the influence of cultural differences on English learning from the perspective of the linguistic and cultural community based on data feature decomposition and fusion clustering is proposed. The new knowledge and the old knowledge are connected through the optimal control of the learning model. By decomposing relevant data through data features and fusion clustering algorithm, the impact of cultural differences on English learning can be modeled and analyzed from the perspective of language and cultural community. The simulation results show that the data fusion performance of the impact model of this method on English learning is good, and the quantitative analysis results are accurate and reliable. According to the results of visual data, targeted interventions were carried out for teachers, learners, and teaching managers.
<b>CSIS Multilateral Cyber Action Committee:</b> The Two Technospheres. Western-Chinese Technology Decoupling: Implications for Cybersecurity. Washington, D.C.: Center for Strategic &amp; International Studies (CSIS), März 2022

Article CSIS Multilateral Cyber Action Committee: The Two Technospheres. Western-Chinese Technology Decoupling: Implications for Cybersecurity. Washington, D.C.: Center for Strategic & International Studies (CSIS), März 2022 was published on September 1, 2022 in the journal SIRIUS – Zeitschrift für Strategische Analysen (volume 6, issue 3).
ensemble approach on predicting blood platelets using supervised approach via computational bio informatics

Platelets are little leukocytes that are essential for such blood coagulation. When an individual does not generate sufficient platelets they won't function effectively, then they will likely to have frequent haemorrhage and bruising since normal deterioration towards the exterior of blood vessels can go inoperable. This may be prescribed to accommodate patients using platelet transfusions via blood donors to halt such haemorrhage. Unfortunately, such medication is not optimal since patients develop antigens against given blood platelets, preventing them from being managed with platelets of similar blood groups in the future. As a result, blood centres are looking towards finding alternatives to donor platelets. Platelet transplants are commonly utilised to reduce or eliminate haemorrhage in a range of patient groups, including those that are currently haemorrhage and those who have malfunctioning or inadequate platelets. Because of the unpredictability of demands, commodity highly perishable, and cost, all institutions and health centres must prioritise estimating hospital-wide platelet consumption. Also, with fast advancement of ML technology, now it is extensively applied in the treatment of a variety of disorders. The benefit of machine learning methods is how they can interpret high-order range of predictor correlations and produce more solid predictions.
The Development of Nutritional Literacy Curriculum with Practical Application on Middle School Students

Identify the impact of a nutrition literacy education program on middle school aged children through hands-on experience and practical nutrition literacy lessons.
DACTYLOLOGY RECOGNITION SYSTEM

Dactylology is the technique of communicating with signs made with fingers, especially in the manual alphabets used by the deaf.Sign Language is one of the oldest and most natural form of language for interaction between normal and deaf &amp; mute people, but since most people do not know sign language and interpreters are very difficult to find, we have come up with a real time method using neural networks for fingerspelling based American sign Language. Sign language is a visual language and consists of 3 major components: Fingerspelling, Word level vocabulary and Facial expressions. In our project we basically focus on producing a model which can recognise Fingerspelling based hand gestures in order to form a complete word by combining each gesture. We follow 4 steps in the hand gesture recognition: i) Data Acquisition, ii) Data preprocessing, iii) Feature extraction iv) Gesture classification. We aim to collect 600 RGB images each for 26 alphabets and apply a filter called Gaussian blur to each of the images. This filter gives a black&amp; white outline of the hand gesture which makes it easier for computation. The images are then passed through convolution neural network classifier which predicts the class of the hand gestures. Our method provides 95% accuracy for the 26 letters of the alphabet
Architecture Design Development of e-Learning for Primary School Learning in Madiun City

Learning quality can be improved by utilizing technology and digital media in teaching and learning. Based on a survey conducted in 2021 at 33 elementary schools in Madiun City, most teachers still use WhatsApp Group media in delivering material. However, schools with good technological facilities can better understand technology for teachers and students. This research is downstream from previous research that recommended the development of LMS-based e-learning in elementary schools in Madiun City. The importance of good management in documenting the teaching and learning process can be used as monitoring and evaluation material to improve the quality of learning. Data collection in this study was carried out by direct observation at SDN 01 and SDN 03 Manisrejo Madiun City, as well as surveys conducted at 33 elementary schools in the Madiun City Education Office. The results of this research are internal and external condition analysis, business process analysis, Data, and Information architecture design, application architecture design, technology architecture design, and people architecture design which can be used as Pecel-AE application development framework documents.
REAL TIME FINGER WRITING RECOGNITION USING WEBCAMERA

This paper proposes a real time finger writing recognition system using a web camera to enable user to interact with computer systems by their own bare hands. The hand region is firstly extracted by background subtraction and blob labelling . Then, convex hull and convexity defect are used to count the fingers and detect the fingertip coordinates. The trajectory of the fingertip is smoothed and drawn to become a finger writing character. This paper describes a system which uses multiple visual process to detect like Hand Tracking, Volume Gesture, Finger counter which ultimately tracks Finger Recognition based on computer vision. In modern technologies video tracking and processing the feed has been very essential. This processed data can be used for many research purposes or to express a particular output on a particular system. There are various methods for processing and manipulation of data to get the required output. This application is created using OpenCV module and Mediapipe. OpenCV is a computer vision and machine learning software library that includes many common image analysis algorithms that will help us build custom intelligent computer vision applications. In this application frequent image feed results in video tracking of our particular finger. Video tracking is the process of locating a moving object (or multiple objects) over time using a camera. The ability to perceive the shape and motion of hands can be a vital component in improving the user experience across a variety of technological domains and platforms .One such technology is using MediaPipe .MediaPipe is a framework mainly used for building audio, video, or any time series data. With the help of the MediaPipe framework, we can build very impressive pipelines for different media processing functions.
TEXT CLASSIFICATION USING DEEP LEARNING

We introduce a novel approach for automatically classifying the sentiment of Twitter messages using NLP and deep learning. These messages are classified as either positive or negative with respect to a query term using sentiment analysis. This is useful for consumers who want to research the sentiment of products before purchase, or companies that want to monitor the public sentiment of their brands. There is no previous research on classifying sentiment of messages on microblogging services like Twitter. We present the results of deep learning algorithm for classifying the sentiment of Twitter messages using distant supervision. Our training data consists of Twitter messages. This type of training data is abundantly available and can be obtained through automated means. We show that deep learning algorithm LSTM which is a variant of recurrent neural network (RNN) have accuracy approximately 80% when trained with this data. This paper also describes the preprocessing steps needed in order to achieve high accuracy. The main contribution of this paper is the idea of using tweets for distant supervised learning.
Armaments Detection Using Artificial Intelligence and Deep Learning for Security Application

Abstract: Security is usually a main concern in every domain, thanks to a rise in crime rate in a crowded event or suspicious lonely areas. Abnormal detection and monitoring have major applications of computer vision to tackle various problems. Thanks to growing demand in the protection of safety, security and private properties, needs and deployment of video surveillance systems can recognize and interpret the scene and anomaly events play an important role in intelligence monitoring. This project implements automatic gun (or) weapon detection employing a Convolution Neural Network (CNN) based Single Shot Multibox Detector and Faster R-CNN algorithms. Proposed implementation uses two sorts of datasets. One dataset, which had pre-labeled images and therefore the other one, may be a set of images, which were labeled manually. Results are tabulated, both algorithms achieve good accuracy, but their application in real situations is often based on the trade-off between speed and accuracy. The system is entailed with automatic detection of the handgun/ knife or the other crime objects without manual intervention. This technique operates directly on the raw inputs i.e. security footage of camera in live; thus is proven to exhibit increased accuracy thanks to usage of datasets and instant identification.
Research and development of Integrated drilling engineering collaborative design system

In view of the present domestic research status of drilling design, based on the characteristics of drilling engineering design under network environment, this paper puts forward the integration under the network environment the driller. Using computer database technology and network technology, the unified hardware and software platform to build design basis and data support, drilling engineering design, the cementing design, the functions and main features of the system are described in detail. The application of this system, enables multiple drilling engineering design or distribution in different parts of the design personnel, in the design and drilling engineering and geological drilling process fully.
Secured Quantum Key Distribution Encircling Profuse Attacks and Countermeasures

To enhance security in today’s computerized environment, communication must go above the boundaries of protocols. On the one extreme, improvements in encryption technology have been achieved, while the integrity of traditional techniques has indeed been repeatedly violated on another. In an online shopping, financial activity, or exchanging message communication via networks, safe transmission or activity should be considered at all times. The traditional cryptography’s integrity is frequently reliant on computational limitations. The safety of the RSA system, one of most frequently used public-key encryption technique, is predicated on factoring’s claimed difficulty. As a result, traditional encryption is followed more by possibility of unanticipated developments that may be hacked or attacked in quantum code-breaking technology and techniques. In the proposed system of quantum key distribution, the Trojan horse attack and time-shift attack are discussed. Quantum key distribution (QKD) is often recognized as a way to provide encrypted systems or communication services that assure communication security and reliability. Algorithms based on the concepts of quantum key distribution have showed promise in the pursuit for a higher security approach. So, the proposed quantum key distribution (QKD), the best aspect of quantum cryptography, decides to make cryptography unconditionally secure in communication of the networks. This suggested system also addresses quantum key distribution protocols in the context of significant network loss, as well as different threats and countermeasures that are taken into concern. They produce a safe key by using traditional post-processing techniques like error checking and safety amplification. By using protocols, these keys can be utilized for making transmission completely trustworthy.
SEMI DECOUPLED DECOMPOSITION FOR IMPERFECT IMAGE AUGMENTATION

Low light augmentation has a consequence for displaying the image with high-quality and greater visibility.While maintaining the visual appearance clear it is difficult to increase the quality.Haze removal techniques are used to improve the attributes of the dark images, but this method can lose some features because of reduced adaptableness under low light conditions.Retinex-based methods are suitable for soft light augmentation, but they still have some limits.Due to less quality of image breakdown, the enhanced result will be affected by noise.To avoid these problems a new technique of image augmentation is attained efficiently.The layer of illumination is obtained from an input image by using the Gaussian total variation model, and the layer of reflectance is estimated from an input image and layer of illumination.Noise is also reduced during the estimation of the layer of reflectance.The quality of enhancement is compared with the Haze removal techniques by considering five low-light imperfect images that are collected from the internet data source.The proposed method provides better performance metrics (PSNR-64.26,MSE-0.02, and SSIM-0.73)when compared to the existing methods.
OADE-Net: Original and Attention-Guided DenseNet-Based Ensemble Network for Person Re-Identification Using Infrared Light Images

Recently, research on the methods that use images captured during day and night times has been actively conducted in the field of person re-identification (ReID). In particular, ReID has been increasingly performed using infrared (IR) images captured at night and red-green-blue (RGB) images, in addition to ReID, which only uses RGB images captured during the daytime. However, insufficient research has been conducted on ReID that only uses IR images because their color and texture information cannot be identified easily. This study thus proposes an original and attention-guided DenseNet-based ensemble network (OADE-Net)—a ReID model that can recognize pedestrians using only IR images captured during the day and night times. The OADE-Net consists of the original and attention-guided DenseNets and a shallow convolutional neural network for the ensemble network (SCE-Net), which is a model used for combining the two models. Owing to the lack of existing open datasets that only consist of IR images, the experiments are conducted by creating a new dataset that only consists of IR images retrieved from two open databases (DBPerson-Recog-DB1 and SYSU-MM01). The experimental results of the OADE-Net showed that the achieved ReID accuracy of the DBPerson-Recog-DB1 is 79.71% in rank 1, while the mean average precision (mAP) is 78.17%. Furthermore, an accuracy of 57.30% is achieved in rank 1 in the SYSU-MM01 case, whereas the accuracy of the mAP was 41.50%. Furthermore, the accuracy of the OADE-Net in both datasets is higher than that of the existing score-level fusion and state-of-the-art methods.