Reviewerly/exHarmony huggingface dataset:
Human Emotion Recognition from Body Posture with Machine Learning Techniques

Recently, increasing attention in the field of gesture recognition, has become a key strategy in analyzing the emotional states of human body movements for social communication. Most real-life scenarios include identifying emotions from facial expressions, vocal synthesis, hand recognition and body gestures. The body posture powerfully conveys the micro emotions of a person in depth. The prediction of human - gait is significantly harder, because the pattern of the human pose estimation has additional degrees of self-determination than the facial emotions, and the overall shape varies robustly during the articulated motion. In this paper, we propose a novel method to recognize 17 different micro emotions from GEMEP dataset based on human upper body gestures dynamics features extracted from the abstract representations of patterns from videos. In the experimental results, KNN exhibit the proposed architecture's effectiveness with an accuracy rate of 97.1% for the GEMEP dataset, 95.2% for SVM, 51.6% for Decision Tree and 49.7% Naive Bayes, respectively.
Perancangan Game Edukasi Pengenalan Bahasa Pemrograman Menggunakan Construct 2

Coding or computer programming for children is now increasingly popular. According to various studies, children who learn coding at a very young age will benefit from learning to critically assess situations, explore different perspectives, build creative solutions, and even develop cognitive abilities. But of course the difficulty of the learning process itself is another level, and again, we need to see the fact that children nowadays are more into video games than learning makes this kind of situation become more complex. Therefore, this research was carried out which aims to design and create an Android-based coding introduction educational game called Funcode. Game development is assisted by the Construct 2 game engine and using the waterfall method from the SDLC or System Development Life Cycle. The game is in the form of a module equipped with a quiz by introducing HTML and CSS programming languages, as well as applying a little Computational Thinking material. Based on the blackbox test results, this mobile game called Funcode is successfully runs without any errors on various types of smartphones that have been tested
Design and Research of Computer Network Education and Teaching System Based on Multisensor Information Fusion

With the rapid development of information technology, information network has become the fundamental guarantee of social progress. The network is the basic material foundation of the information society. It is ubiquitous, and human activities are highly dependent on it. Education is also inseparable from the Internet. Online learning is emerging as a new form of basic education. At the same time, it needs to be considered that the information received by the sensor is always incomplete, reflecting the information measured in a particular part of the object but not understanding the total content. Therefore, it is necessary to use multiple sensors to identify an object from multiple angles, obtain additional dimensional information about the object, and combine multidimensional information. This paper introduces the design and implementation of a teaching network system for computer network courses, starting from the overall concept of the system, the selection of development methods and the specific implementation plan of the system, and then introduces the specific content of each module in the teaching system in detail. The recognition success rates of five groups of experiments based on multisensor information fusion are 96.9%, 95.7%, 98.4%, 96.3%, and 98.7%, respectively.
Emerging Trends in Classification with Imbalanced Datasets: A Bibliometric Analysis of Progression

Imbalanced or unbalanced datasets are defined as the highly skewed distribution of target variable in the field of machine learning. Imbalanced datasets have greatly caught the attention of researchers due to their negative effect on machine learning models in the last decade. Researchers develop various solutions to the problems of imbalanced datasets and contribute to the literature.The increasing number of articles makes it difficult to follow the literature. Review articles contribute to the solution of this problem. The goal of this study is to conduct a bibliometric analysis to find solutions for classification with imbalanced datasets. Bibliometric analysis is a quantitative technique based on extracting statistics from databases. This work is the first bibliometric analysis to address the problem of imbalanced datasets. In this study, data on imbalanced datasets were obtained from the Scopus database with the R Bibliometrix package version 3.1.4, and recent studies and new approaches were summarized. Data on 16255 publications between 1957-2021 were collected by using selected keywords. This collection mainly comprises 8871 articles, 6987 conference papers, and 175 reviews with 1, 66 average citations per year per document. Among the most cited countries, the United States has 106139 total citations followed by China with 13839 citations and Germany has 9524 citations.
Leveraging Deep Features Enhance and Semantic-Preserving Hashing for Image Retrieval

The hash method can convert high-dimensional data into simple binary code, which has the advantages of fast speed and small storage capacity in large-scale image retrieval and is gradually being favored by an increasing number of people. However, the traditional hash method has two common shortcomings, which affect the accuracy of image retrieval. First, most of the traditional hash methods extract many irrelevant image features, resulting in partial information bias in the binary code produced by the hash method. Furthermore, the binary code made by the traditional hash method cannot maintain the semantic similarity of the image. To find solutions to these two problems, we try a new network architecture that adds a feature enhancement layer to better extract image features, remove redundant features, and express the similarity between images through contrastive loss, thereby constructing compact exact binary code. In summary, we use the relationship between labels and image features to model them, better preserve the semantic relationship and reduce redundant features, and use a contrastive loss to compare the similarity between images, using a balance loss to produce the resulting binary code. The numbers of 0s and 1s are balanced, resulting in a more compact binary code. Extensive experiments on three commonly used datasets—CIFAR-10, NUS-WIDE, and SVHN—display that our approach (DFEH) can express good performance compared with the other most advanced approaches.
Distantly Supervised Named Entity Recognition with Self-Adaptive Label Correction

Named entity recognition has achieved remarkable success on benchmarks with high-quality manual annotations. Such annotations are labor-intensive and time-consuming, thus unavailable in real-world scenarios. An emerging interest is to generate low-cost but noisy labels via distant supervision, hence noisy label learning algorithms are in demand. In this paper, a unified self-adaptive learning framework termed Self-Adaptive Label cOrrection (SALO) is proposed. SALO adaptively performs a label correction process, both in an implicit and an explicit manners, turning noisy labels into correct ones, thus benefiting model training. The experimental results on four benchmark datasets demonstrated the superiority of SALO over the state-of-the-art distantly supervised methods. Moreover, a better version of noisy labels by ensembling several semantic matching methods was built. Experiments were carried out and consistent improvements were observed, validating the generalization of the proposed SALO.
Design of Game Data Acquisition System Based on Artificial Intelligence Algorithm

Game data collection system is a tool used to collect the behavior data of users about the game. It can be used for data analysis of user behavior so that game manufacturers can keep abreast of market dynamics and popular trends, and they also can have a deeper understanding of the behavioral habits and psychology of player user groups. The defects of the current data acquisition system include that the data are not encrypted. The network transmission efficiency is relatively low. The acquisition speed is slow, and the settings cannot be dynamically changed. This paper proposes to study how to enhance the acquisition ability and improve the analysis efficiency in the design of data acquisition system for solving these problems. Therefore, on the basis of artificial intelligence algorithm, this paper designs a game data collection system by using artificial neural network algorithm, support vector algorithm, and cluster analysis algorithm, which solves the basic problem of slow data collection in current data collection and plays a role in improving the efficiency of network transmission. The experimental results in this paper show that when the number of data is more than 300, the time-consuming time reaches more than 68 ms. When the number of written data is more than 300, it takes more than 181 ms. When the number of deleted data is more than 300, it takes more than 236 ms. From the above data, it shows that the designed game data collection system is rapid and efficient.
Personalized Item Recommendation Algorithm for Outdoor Sports

With the rapid development of China's economy, people are eager for an effective way to relieve work pressure and strengthen their health at the same time. Outdoor sport is one of the best choices for people. However, the amount of recommended data on the network is very large. As a result, when people understand outdoor sports through the network, they cannot effectively obtain the information they want. This is the problem of "information overload," and personalized recommendation system can effectively alleviate this problem. In order to effectively recommend outdoor sports to users, a useful attempt was made in the personalized recommendation system for outdoor sports in this paper. The specific work of this paper is as follows: firstly, the current situation of outdoor sports in China was summarized, and the related technologies of the recommendation system were studied, including user modeling technology, recommendation target modeling technology, and recommendation algorithm. In order to obtain better recommendation effect, this paper proposes to mix user-based collaborative filtering recommendation algorithm, project-based collaborative filtering recommendation algorithm, and content-based recommendation algorithm. The hybrid algorithm adopts the way of feature expansion and weighted combination. Firstly, the hybrid model (model 1) of user-based collaborative filtering recommendation and content-based recommendation is obtained. Secondly, the hybrid model (model 2) based on project collaborative filtering recommendation and content-based recommendation was obtained. Finally, model 1 and model 2 were combined together to get a hybrid model with better final recommendation effect. For the common cold start problem in the recommendation system, the system adopts content-based recommendation algorithm to solve it.
Educate to transform: An innovative experience for faculty training

Learning-focussed educational models require the development of pedagogical, methodological, assessment and technological competences among the faculty community. The COVID-19 pandemic has accentuated the need for this training. This study evaluates the impact of the training project "Educate to Transform" on teacher attitudes, knowledge and on the implementation of innovative teaching methodologies. There were 695 faculty participants in the program conducted at the Universidad Francisco de Vitoria (UFV). Participants included full-time professors (FULL-PROF) and part-time professors (PART-PROF). The measurement instrument was validated using the entire sample and a subsample of 357 participants was used to analyse the impact of the program (pre and post measurement). Professor attitudes and knowledge of or familiarity with innovative methodologies and their application in the classroom were evaluated. The findings show that the program improved the attitudes of teachers towards innovation, raised the level of awareness and number of methodologies implemented in the classroom. The methodology towards more experiential and collaborative learning is effective in transforming teaching practice. Furthermore, the implementation of the program through the CANVAS platform, making teachers live the experience as learners, seems to have contributed to improve the teachers' attitude towards the LMS. The only difference found among the participants was a worse attitude towards innovation on the part of medical teachers, with a clearly differentiated profile of teachers and students, as well as a greater implementation of active methodologies by teachers with a lower teaching load. Overall, it may be concluded that the program achieved its proposed objectives.
New approach on global optimization problems based on meta-heuristic algorithm and quasi-Newton method

&lt;p&gt;This paper presents an innovative approach in finding an optimal solution of multimodal and multivariable function for global optimization problems that involve complex and inefficient second derivatives. Artificial bees colony (ABC) algorithm possessed good exploration search, but the major weakness at its exploitation stage. The proposed algorithms improved the weakness of ABC algorithm by hybridized with the most effective gradient based method which are Davidon-Flecher-Powell (DFP) and Broyden-Flecher-Goldfarb-Shanno (BFGS) algorithms. Its distinguished features include maximizing the employment of possible information related to the objective function obtained at previous iterations. The proposed algorithms have been tested on a large set of benchmark global optimization problems and it has shown a satisfactory computational behaviour and it has succeeded in enhancing the algorithm to obtain the solution for global optimization problems.&lt;/p&gt;
Feature selection of unbalanced breast cancer data using particle swarm optimization

&lt;p&gt;Breast cancer is one of the significant deaths causing diseases of women around the globe. Therefore, high accuracy in cancer prediction models is vital to improving patients’ treatment quality and survivability rate. In this work, we presented a new method namely improved balancing particle swarm optimization (IBPSO) algorithm to predict the stage of breast cancer using unbalanced surveillance epidemiology and end result (USEER) data. The work contributes in two directions. First, design and implement an improved particle swarm optimization (IPSO) algorithm to avoid the local minima while reducing USEER data’s dimensionality. The improvement comes primarily through employing the cross-over ability of the genetic algorithm as a fitness function while using the correlation-based function to guide the selection task to a minimal feature subset of USEER sufficiently to describe the universe. Second, develop an improved synthetic minority over-sampling technique (ISMOTE) that avoid overfitting problem while efficiently balance USEER. ISMOTE generates the new objects based on the average of the two objects with the smallest and largest distance from the centroid object of the minority class. The experiments and analysis show that the proposed IBPSO is feasible and effective, outperforms other state-of-the-art methods; in minimizing the features with an accuracy of 98.45%.&lt;/p&gt;
Gender detection in children’s speech utterances for human-robot interaction

The human voice speech essentially includes paralinguistic information used in many real-time applications. Detecting the children’s gender is considered a challenging task compared to the adult’s gender. In this study, a system for human-robot interaction (HRI) is proposed to detect the gender in children’s speech utterances without depending on the text. The robot's perception includes three phases: Feature’s extraction phase where four formants are measured at each glottal pulse and then a median is calculated across these measurements. After that, three types of features are measured which are formant average (AF), formant dispersion (DF), and formant position (PF). Feature’s standardization phase where the measured feature dimensions are standardized using the z-score method. The semantic understanding phase is where the children’s gender is detected accurately using the logistic regression classifier. At the same time, the action of the robot is specified via a speech response using the text to speech (TTS) technique. Experiments are conducted on the Carnegie Mellon University (CMU) Kids dataset to measure the suggested system’s performance. In the suggested system, the overall accuracy is 98%. The results show a relatively clear improvement in terms of accuracy of up to 13% compared to related works that utilized the CMU Kids dataset.
A hybrid framework for detecting structured query language injection attacks in web-based applications

&lt;p&gt;&lt;span&gt;Almost every web-based application is managed and operated through a number of websites, each of which is vulnerable to cyber-attacks that are mounted across the same networks used by the applications, with much less risk to the attacker than physical attacks. Such web-based attacks make use of a range of modern techniques-such as structured query language injection (SQLi), cross-site scripting, and data tampering-to achieve their aims. Among them, SQLi is the most popular and vulnerable attack, which can be performed in one of two ways; either by an outsider of an organization (known as the outside attacker) or by an insider with a good knowledge of the system with proper administrative rights (known as the inside attacker). An inside attacker, in contrast to an outsider, can take down the system easily and pose a significant challenge to any organization, and therefore needs to be identified in advance to mitigate the possible consequences. Blockchain-based technique is an efficient approach to detect and mitigate SQLi attacks and is widely used these days. Thus, in this study, a hybrid method is proposed that combines a SQL query matching technique (SQLMT) and a standard blockchain framework to detect SQLi attacks created by insiders. The results obtained by the proposed hybrid method through computational experiments are further validated using standard web validation tools.&lt;/span&gt;&lt;/p&gt;
Secure deep learning for distributed data against maliciouscentral server

In this paper, we propose a secure system for performing deep learning with distributed trainers connected to a central parameter server. Our system has the following two distinct features: (1) the distributed trainers can detect malicious activities in the server; (2) the distributed trainers can perform both vertical and horizontal neural network training. In the experiments, we apply our system to medical data including magnetic resonance and X-ray images and obtain approximate or even better area-under-the-curve scores when compared to the existing scores.
Brace yourself! Why managers should adopt a synthetic media incident response playbook in an age of falsity and synthetic media

Synthetic media presents looming threats to managers in a business setting. To address this issue, we first offer a short overview of the evolution of media manipulation to contextualize the new era of synthetic media. Then, we present the problems associated with synthetic media via veridicality and heuristics to illustrate how consumers have little choice but to believe what they see, read, and hear online. We outline the most likely and impactful types of synthetic media threats and attacks and present a synthetic media incident response playbook. Our aim is to inform managers about six specific phases so they can prepare, assess, detect, analyze, and recover from synthetic media incidents and coordinate their lessons learned.
Literasi Digital di Era Pembelajaran Abad 21

&#x0D; &#x0D; This community service activity was carried out to provide socialization on the importance of understanding digital literacy in dealing with learning in the 21st century era as an effort to improve students' abilities through reading literacy activities so as to foster interests and talents in students from an early age as well as a literacy culture that is embedded in students. can affect the level of success and the ability of students to understand information analytically, critically, and reflectively. This community service activity was carried out at the Mekar Mukti 02 State Elementary School, North Cikarang. Some literacy support programs that can be applied in elementary schools include getting used to reading 15 minutes before learning has started, so that students' reading interest and reading skills increase and master better.&#x0D; Keywords: Digital Literacy, Socialization, 21st Century Learning&#x0D;
X-Ray Small Target Security Inspection Based on TB-YOLOv5

Security inspection is extremely important for the safety of public places. In this research, we are trying to propose a novel algorithm and investigated theoretically in the X-ray dataset, which can optimize the relative low detection accuracy and the latent omission detection of smaller objects when using You Only Look Once version 5 (YOLOv5). For one side, the transform detection network is selected to be added at the bottom layer of backbone structure to avoid the loss of useful information during sequential calculation. On another side, we attempt to adjust the existing PANet structural elements of the model, including their connections and other related parameters to improve the detection performance. We integrate an efficient BiFPN with the CA mechanism, which can enhance feature extraction, and named it attention-BiFPN. Experimental consequences demonstrate that the detection accuracy of the proposed model, which we name “TB-YOLOv5,” has obvious advantages in check performance compared with the mainstream one-stage object detection models. Meanwhile, compared with YOLOv5, the data results display an improvement of up to 14.9%, and the average precision at 0.5 IOU even reached 23.4% higher in the region of small object detection. Our purpose was to explore the potential of changing a popular detection algorithm such as YOLO to address specific tasks and provide insights on how specialized adjustments can influence the detection of small objects. Our work can supply an effective method of enhancing the performance of X-ray security inspection and show promising potential for deep learning in related fields.
Modeling of Interactive Teaching and Learning System for Students Based on Artificial Intelligence

In order to improve the interactive effect of modern teaching, this paper combines the artificial intelligence interactive technology to model the teaching interactive process and designs and analyzes the system static test of the infrared feature threshold online monitoring system. Moreover, this paper performs beam-expanding shaping on the output feature and increases the shaping optical path and the uniform optical path to make the infrared spot energy distribution relatively uniform and reduce the error when observing the loss threshold. The software part takes the spot shift threshold and quadrant amplitude threshold as the design basis of the upper computer threshold judgment algorithm. In addition, this paper designs an interactive teaching and learning system for students based on artificial intelligence. The research results show that the interactive teaching and learning system for students based on artificial intelligence has an obvious role in promoting modern teaching.