Reviewerly/exHarmony huggingface dataset:
Cyber-social-technological-cognitive (CSTC) approach in ecosystems: trends and challenges

This lecture aims to provide an international forum to discuss the main ideas about cyber-social-technologicalcognitive approach present in indifferent ecosystems. Nowadays, investments that promote disruptive technologies and digital transformation accompanied by advances in Industry 4.0 enable the development of intelligent and smart systems in different domains. The hyperspace allows the interconnection of multiple spaces of computers and networks which are interlinked with each other in cyber, social, technological and cognitive domains. The CTSC approach is present today in indifferent ecosystems. In this context, conceptual models and architectures that integrate relevant attributes present in hyperspace are discussed focusing in Smart Ports, Green Energy Systems, and Health and Wellness Systems. In the case of Smart Ports, the Fifth generation ports are automated and sensorized, intensive in the use of artificial intelligence tools, sensors, RFID radio frequency, differential GPS system, Internet of Things (IoT), data management for Big Data, among others. Moreover, Green energy system's cyber domain enables technologies and processes to operate seamlessly such as grid flexibility enabled by IoT, and digital networks; smart metering; and the new concept of energy consumer producer “prosumer”. In the field of Health and Wellness Systems the pandemic has given a great opportunity to CSTC in the application of new methods, models, practices, and technologies such as Robotics, Virtual and Augmented Reality, Big Data, Artificial Intelligence, and Analytics. All of them have extraordinary potential in the field of medicine as it allows to analyze large volumes of data in order to predict, prevent or customize treatments in different pathologies. Today different open source data platforms and AI are used to track the spread of the disease. The role of the Internet of services (IoS) stands out as an ecosystem that takes over complex collaborative applications using interoperable resources across platforms and cloud storage. It also addresses advances in AI, neuroscience, and cognitive sciences that enable the development of Human Body Communication (HBC) and Human Information Processing (HIP) and open the door to multiple applications in the Internet of the People (IoP) domain.
Photographic Image Intelligent Fuzzy Assistant Teaching System Based on Augmented Reality and Web

Due to the limitation of time and space, the traditional photographic image intelligent fuzzy teaching system can not provide targeted auxiliary teaching for students with different learning abilities. The design of intelligent fuzzy assistant teaching system of photographic image based on augmented reality and web is carried out, including the hardware design of server and peripheral equipment selection, and the design of intelligent fuzzy assistant teaching system of photographic image based on Web and augmented reality, such as intelligent fuzzy assistant teaching display, student learning behavior evaluation software design, etc. Experiments show that, compared with the traditional system, the photographic image intelligent fuzzy assistant teaching system based on augmented reality and web has more correct allocation of teaching resources, stronger pertinence and practical value.
Artificial Intelligence: Pertinence in Supply Chain and Logistics Management

Artificial Intelligence (AI) is the revolutionary invention of human intelligence. Artificial Intelligence is nothing but the duplication of human in which machines are programmed to rationally think and behave like humans developed for very many purposes including business decision making, problem-solving, business data analysis and interpretation and information management. The application of AI in business endeavors decides the competitive advantage, market leadership, robust operating efficiency of corporate and other business houses. Exploiting the application of AI in the manufacturing and distribution process enables the organisations to reach the pinnacle in their business graph. Businesses are operating in the international market which is highly multifaceted and challenging to serve the world as a sole market for their products, services and their products and without the integration of technology into their business processes, they cannot assure the sustainable growth. The management of the process of transforming the raw materials into the final product is called Supply Chain Management (SCM) and the effective movement and storage of goods, services and information are called Logistics Management (LM). This article analyses the applications of Artificial Intelligence in Supply Chain and Logistics Management (SC&LM).
A Survey on Automated Student Attendance Management System Using Face Recognition

Maintaining attendance system of students of various classes and accessing them is a difficult task. Calling names of each student in each hour and keeping a record of the attendance is time consuming as well. Other issues would be false sign, name missing out in sheets, manually entering data to computers and there is also a chance of proxy attendance. The following system marks attendance based on the concept of face detection and face recognition. The daily attendance of all the students is noted subject wise and is stored by the administrator. Hence, accessing as well as storing the attendance system is easier and more efficient. It makes effective and perfect application of man power and resources.
Human Emotion and Healing in Opera - Focusing on the Scene of Aria ‘Vesti la giubba’ from Opera 〈I Pagliacci〉 in Animation 〈Opera Imaginaire〉

The purpose of this paper is to examine human emotion and healing element in opera, focusing on the scene of aria ‘Vesti la giubba’ from opera 〈I Pagliacci〉 in animation 〈Opera Imaginaire〉. Opera 〈I Pagliacci〉, verismo opera that realistically expresses the dark and sad emotions of human life in tragedy and music, depicts the grief and futility of a clown life. The scene of ‘Vest la giubba’ in animation 〈Opera Imaginaire〉 which visualizes the contents of opera 〈I Pagliacci〉 depicts the tragedy of life, emphasizes the appearance of the protagonist Canio and his feelings, and offers healing elements through a different ending from the original. Aria ‘Vesti la giubba’ expresses Canio’s emotion, who has to hide his sadness and go on the stage as a clown through the conversation-type lyrics, high-pitched music and segmented rhythm. And animation 〈Opera Imaginaire〉 adapted from aria depicts Canio’s longing for his wife, betrayal, outrage that lead to murder, and regret and reflection that come after killing his wife. Various images in the scene contain symbols of hope, disconnection, life, anger, sadness and regret. Also the images containing the symbols express emotional change, the progress of events, reconciliation and reflection. In addition the last scene presents Canio’s wish in the form of fantasy, drawing sympathy from people. While watching this animation, people follow the feeling of the main character and release their own emotions. As such, this work provides a musical experience, and a new ending expresses as a symbol, and through the appreciation, people are able to experience the healing through catharsis and reflection on human life.
MoveAR: Continuous Biometric Authentication for Augmented Reality Headsets

Augmented Reality (AR) headsets are rapidly coming to consumer and professional markets. The lack of traditional input interfaces for these devices motivates the need to research novel methods of achieving security primitives such as user authentication. Given the various inertial sensors that the headsets use to position users in their environment, we propose, investigate, and evaluate the potential for a continuous biometric authentication system based on the distinct ways in which people move their heads and interact with their virtual environments. We collect samples of the spatial and behavioural patterns from a group of users wearing an AR headset. Using this data, we propose a multitude of novel models and machine learning pipelines that learn the unique signature of AR users as they interact with the virtual environment and AR objects. We evaluate multiple supervised machine learning algorithms, including k-Nearest Neighbours, Random Forest, Support Vector Machine (SVM), and Bag of Symbolic-Fourier-Approximation Symbols (BOSS) for two different sets of input data and parameters. We achieve a balanced accuracy score of 92.675% and an EER of 11% using an Adaptive Boost Random Forest classifier together with our proposed series of novel, AR-specific preprocessing methods used on our current dataset. This demonstrates that it is indeed possible to profile and authenticate AR head-mounted display users based on their head movements and gestures.
Effect of experimental alteration of apparent Re-Os age and near-infrared transparency of molybdenite

Experiments have been carried out in order to clarify the effect of alteration on Re-Os age and near-infrared (N-IR) transparency of molybdenite. The sample is from Brejui, Brazil, and is opaque to N-IR light. The sample molybdenite was placed in various mediums, such as NaCl, NaHCO3, CaCl2, and AlCl3 solutions and H2O, in sealed quartz tubes and heated at 180 °C for 20 days. Re-Os ages of all the products from experimental alteration showed deviation from that of the original molybdenite, suggesting that Re and Os were fractionated during the experimental alteration. Accordingly, the N-IR transparency of the products increased in most cases. On the other hand, molybdenite altered in NaHCO3 remained opaque despite its Re loss due to alteration. This means that N-IR transparency of molybdenite does not necessarily reflect alteration and Re loss. Although alteration degrades reliability of Re-Os chronometry, Re and Os fractionation can and have been detected by duplicate analysis of the same sample. From this, it can be argued that molybdenite which gives the same Re-Os ages by duplicate analysis, has experienced no significant alteration since its primary mineralization.
E-Mail Spam Classification using Naive Bayesian Classifier

— E-mail spam is the very recent problem for every individual. The e-mail spam is nothing it’s an advertisement of any company/product or any kind of virus which is receiving by the email client mailbox without any notification. To solve this problem the different spam filtering technique is used. The spam filtering techniques are used to protect our mailbox for spam mails. In this project, we are using the Naïve Bayesian Classifier for spam classification. The Naïve Bayesian Classifier is very simple and efficient method for spam classification. Here we are using the Lingspam dataset for classification of spam and non-spam mails. The feature extraction technique is used to extract the feature. The result is to increase the accuracy of the system.
Computer Visualization Technology for Distribution Network Dispatching of Smart Grid

In order to overcome the problem of overload caused by traditional visualization technology, a computer visualization technology for smart grid distribution network scheduling is proposed. After defining the data set of distribution network dispatching, the visualization parameters of smart grid dispatching are extracted. By using the main layout form of the visualization data, the visualization cognitive schema of distribution dispatching component information is set, corresponding to different forms of regulation strategies, the distribution network dispatching visualization platform is built, the function realization code is called, and finally the design of computer visualization technology is completed. After preparing the dispatching data in the region, the node structure in the power grid region is set, and two kinds of traditional visualization technology and the designed visualization technology are prepared for experiments. The experimental results show that the designed visualization technology generates 23.4kW load in the practical application process, and the load generated by the computer is the smallest.
Pengaruh Pembelajaran Online pada Masa Pandemi Covid-19 terhadap Hasil Belajar Mata Pelajaran Ilmu Pengetahuan Sosial Siswa Kelas VIII di SMP Walisongo Bekasi

Basically learning outcomes are the interaction of teaching and learning activities that produce new attitudes and skills from a lesson. The purpose of this study was to find out how well online learning during the Covid-19 pandemic period affected student learning outcomes in social science subjects. This research is included in the comparative quantitative notion, the researcher wants to know whether there is an influence on the determined variables. The research source was the eighth grade students of SMP Walisongo Bekasi, amounting to 35 students. The results showed that the effect of online learning during the Covid-19 emergency on student learning outcomes in social science subjects affected student learning outcomes. This is evidenced by the acquisition of sig = 0.004&gt; 0.05 and Fcount = 2.491.
Concepts, Key Challenges and Open Problems of Federated Learning

With the modern invention of high-quality sensors and smart chips with high computational power, smart devices like smartphones and smart wearable devices are becoming primary computing sources for routine life. These devices, collectively, might possess an enormous amount of valuable data but due to privacy concerns and privacy laws like General Data Protection Regulation (GDPR), this enormous amount of very valuable data is not available to train models for more accurate and efficient AI applications. Federated Learning (FL) has emerged as a very prominent collaborative learning technique to learn from such decentralized private data while reasonably satisfying the privacy constraints. To learn from such decentralized and massively distributed data, federated learning needs to overcome some unique challenges like system heterogeneity, statistical heterogeneity, communication, model heterogeneity, privacy, and security. In this article, to begin with, we explain some fundamentals of federated learning along with the definition and applications of FL. Subsequently, we further explain the unique challenges of FL while critically covering recently proposed approaches to handle them. Furthermore, this paper also discusses some relatively novel challenges for federated learning. To conclude, we discuss some future research directions in the domain of federated learning.
Compression Techniques for DNA Sequences: A Thematic Review

Deoxyribonucleic acid (DNA) is the basic entity that carries genetic instructions. This information is used in the evolution, progression, and improvement of all species. It is estimated that 10 CD-ROMs are required to store the genomic data of an individual being. With the increase in DNA sequencing equipment, an extensive heap of genomic data is created. The increase in DNA data in public databases is surpassing the rate of growth in storage space, thereby raising a significant concern related to data storage, transmission, retrieval, and search. To reduce the data storage and storage expense, lossless compression procedures were applied. Conventional compression methods are not proficient while compressing the biological data. Hence, several unique and contemporary lossless compression mechanisms were used to achieve improved compression ratio in biological sequences. Here, we scrutinize the diverse existing compression procedures that are appropriate for the compression of DNA sequences. The efficiency of algorithms is compared in terms of compression ratio, the ratio of the capacity of the compressed folder, and compression/decompression time. Main challenges and future research directions in DNA compression are also presented. Emphasis has been given to special references related to contemporary techniques.
Effects of COVID 19 lockdown on the use of LMS platforms for virtual education

The health contingency due to COVID 19 showed the importance of having answers to continue with education in virtual modality at all school levels, in this article an analysis of the variables that are related to the implementation and success of Virtual Environments of Learning (PLE) and the response of undergraduate students from the Faculty of Accounting and Administration of the Autonomous University of Baja California, Mexico, when using technological platforms for the Learning Management System (LMS) to continue education remotely or remotely.The research is from a quantitative approach, and with a non-experimental design, through a modeling of structural equations (SEM-PLS), the variables learning in confinement, knowledge of training needs in contingency, knowledge and mastery of technologies are analyzed, interaction with the educational community and its relationship with LMS development during the contingency, the findings reveal the challenges for digital transformation and educational innovation.
A Systematic Literature Review on Nowcasting for Future Research Directions

Nowcasting is the economics-based discipline, through which a trend or a trend reversal can be determined in real time. The concept of nowcasting is more inclined towards the facts and its major focus is on the aspects that are either known or have the possibility to be known. It can also be considered as the basis for the process of decision making. Nowcasting has found its applications in a few fields such as economics and meteorology. Various models and approaches have been developed and utilized in different contexts especially while estimating the economic situation of different countries. The usage of nowcasting is still limited and there is huge gap of literature regarding this topic. In this regard, the current study has been designed with the aim to explore this concept and its applications in different fields and in relation with machine learning. The review of literature approach has been employed by the researcher by searching the relative material using the related keywords. The study indicates that nowcasting has applications in meteorological and economic domains. Moreover, the review has indicated that wide ranging avenues for research are present in space nowcasting, satellite nowcasting and pandemic nowcasting.
Image Demosaicing: A Roadmap to Peculiarity Imaging

Image demosaicing is the process of reconstruction of full color image on the basis of various methods from the data samples obtained by acquisition devices. This paper deals with the various techniques that can be utilized to achieve demosaiced image by suitably classifying it into various categories. Moreover, it also provides an insight into the evaluation metrics used for confirming the performance of demosaicing techniques. Besides, the potential areas are also investigated for realistic applications where image demosaicing is found to be highly beneficial for healthcare and forensics.
Stability evaluation for text localization systems via metamorphic testing

The success of learning techniques in solving a variety of hard AI problems promotes the flourish of recognition-based applications. Many state-of-the-art text localization systems, which can detect and report the positions of text segments in an image, are mainly implemented with learning-based techniques. Data-driven learning raises a series of questions on how to verify, validate and evaluate such learning-based systems. In this paper, we propose a methodology to automatically evaluate the stability of text localization systems via metamorphic relations, where a stable system should output consistent results for similar inputs with the same text segments. We introduce six metamorphic relations that should be preserved in a stable text localization system and define the corresponding metrics for stability evaluation. With the defined metamorphic relations, we apply metamorphic testing techniques to compare the inputs and outputs to evaluate system stability, and further diagnose the causes of inconsistency. The extensive experimentation on both academic and commercial text localization systems demonstrates the effectiveness of our method on stability evaluation for such systems.
Camera Fingerprint estimation with a Generative Adversarial Network (GAN)

For forensic analysis of digital images or videos, the PRNU or camera fingerprint is the most important characteristics, for source attribution and manipulation localization. Typically, a good estimate of the PRNU is obtained by computing its Maximum Likelihood estimate from noise residuals of a large number of flatfield images captured by the camera. In this paper, we propose a novel approach of estimating the fingerprint of a camera, with a Generative Adversarial Network (GAN). The idea is to let the Generator network learn a distribution, from which PRNU samples will be drawn after training of the two adversarial networks. Experimental results indicate that the GAN-generated PRNU yields state-of-the-art camera identification and manipulation localization results.
THE EFFECTIVENESS OF PRESENTATION, PRACTICE, PRODUCTION (PPP) METHOD IN DEVELOPING STUDENTS’ SPEAKING SKILL AT MA AL-KHAIRAAT LUWUK

This research aims to find out the effectiveness of the Presentation, Practice, Production (PPP) method in developing students’ speaking skills at MA Al-khairaat luwuk. This research employed a pre-experimental research design involving one-group pre-test–post-test design. The independent variable of this research was Presentation, Practice, Production (PPP) method and the dependent variable was students’ speaking skills. The research population was eleventh-grade students of MA Al-khairaat Luwuk. There were two classes in this population namely one religion class consisted of 20 students and one science class consisted of 12 students. The sample of this research was the religion class. The test included pre-test and post-test. Data were analyzed statistically. Having analyzed the data, the result revealed that the t-counted was (9.87) with a degree of freedom 19 and a significance level of 0.05, t-table was (1.72913). Therefore, the t-test result was higher than the t-table. It means that the hypothesis was accepted. In conclusion, the result showed that there was a significant effect using the Presentation, Practice, Production (PPP) method in developing students’ speaking skills at MA Al-khairaat Luwuk.
The Influence of The Question Box Media Assisted Teams Games Tournament Model on Science Learning Outcomes at Elementary School

This study aims to provide an empirical picture of whether or not the Teams Games-Tournament (TGT) learning model aided by Question Box media has an impact on the fourth-grade science learning outcomes at SDN Cengkareng Barat 16 Pagi West Jakarta in the second semester of the 2018/2019 academic year. The research population consisted of 55 students consisting of 28 students of class IV A and 27 students of IV B. The sampling technique used is saturated sampling. This study used a quantitative approach, and the research method used was a quasi-experimental design with a Nonequivalent Posttest-Only Control Group Design. The result showed an effect of the Teams Games-Tournament (TGT) learning model assisted by the question box media on student learning outcomes in science.