Reviewerly/exHarmony huggingface dataset:
Human Activity Recognition using Time Series and Machine Learning Techniques

Human activity recognition (HAR) is the classification of different human activities influenced by their behavior and movements. The number of smartphone users and capacity sensors is increasing, and most users carry their phones. Smartphone data boosts HAR’s prominence and appeal. The gadget is used to recognize gestures or actions and perform specified activities in response to the information acquired by these recognition systems. Our study will examine several literary survey approaches. The KU-HAR dataset has utilized containing sensors data like accelerometers, and gyroscopes in various locations. This research’s goal is to identify human activity in smartphone sensors using machine learning classification algorithms. Sensors data from smartphone accelerometers and gyroscope identify human activity. These data samples are utilized using smartphone sensors. Different machine learning techniques such as Decision Tree, Random Forest (RF), Gradient Boosting, Gaussian NB, KNN, Logistic Regression, and SVC has been applied to classify the human activity and achieved satisfactory outcome. This research can helps researchers to establish an expert system to detect any movements of human.
Magic Mirror by Bidirectional GAN: A Video Data Theft-Proof Framework

Video data is always seen as a kind of high-value data due to its salient features on rich and intuitive presentations, which also makes it an important objective of cyber data theft. Traditionally, people adapt various encryption algorithms to protect sensitive video data. However, such methods yield to intensive computations, complicated key management, and unexpected key leakages. In this paper, we fundamentally change the logic of video protections from stored raw data encryption to machine learning based video frame image learning and retrieval without raw data storage. By building inversions of Generative Adversarial Network (GAN), we firstly transform video’s raw data to a series of parameters in the latent space with raw data discarded. When retrieving, the trained GAN model will reassemble frame images on the basis of latent codes, which is protected by a Honey Encryption (HE) algorithm. Once the retrieving password is correct, the correct latent codes will be selected to generate the right image. Otherwise, wrong latent codes are chosen towards a misunderstanding figure to deceive attackers, therefore achieving a fine-grained video protection with lower storage costs. Our experiment results show that, the framework demonstrates excellent performance regarding security design, indistinguishability of generated fake image, reconstruction accuracy of original images, consistency of the framework, as well as storage cost reductions.
Solar cell parameter identification based on opposition-based chaotic whale optimization algorithm

Whale optimization algorithm (WOA) has been successfully applied to address the engineering optimization problems. However, lacking of population diversity leads to that WOA is easy to fall into local optimum. To address this gap, this paper proposes opposition-based learning chaotic whale optimization algorithm (OBLCWOA), which is an improved form of WOA. OBLCWOA uses chaotic mapping to generate key random parameters and opposition-based strategy is utilized to enhance the diversity of the population. Those two strategies can effectively improve the convergence speed, convergence accuracy and global search capability. Subsequently, the OBLCWOA is simulated using 23 standard test functions, and the results show that the global search capability, convergence speed and convergence accuracy of the OBLCWOA are significantly improved compared with the WOA and QWOA. Finally, three solar cell models are constructed, the parameters of which is identified by OBLCWOA. The simulation data are compared with the measured data by constructing the fitness function. Experimental results provide evidence on the ability of OBLCWOA in solar cell parameter identification.
Correlation between Internet Literacy and Vocabulary Level

In-depth research on reading comprehension receives less attention in basic education, despite the fact that the problem is just as critical, if not more so, in this pandemic era. This study aims to learn the dynamics of reading comprehension to draw a conclusion on the phenomenon. Terry (2022) observed a strong connection between vocabulary and reading comprehension. However, meager studies have correlated two variables – Internet Literacy (IV) and Vocabulary Level (DV) – with combined implications for Reading Comprehension. With the globalization of online learning, urban demography's visibility was catered with 83 Senior High School students as respondents. The data revealed a low correlation. Generally, the high mean for online literacy and high mean in the performed evaluation showed the participants' awareness of how to use the digital side of learning and a decent vocabulary level. Null Hypothesis II was accepted in this research. Interestingly, this low correlation could be affected by the students' internal and external strategies, which created an impetus for their reading comprehension aside from digital utilization.
Pengaruh Penggunaan Media Manipulatif Terhadap Kemampuan Mengenal Konsep Bilangan Pada Anak Usia Dini di TK Kartini Nagrak Selatan Sukabumi

The purpose of this study was to determine the initial ability to recognize the concept of numbers in early childhood in the control group and in the experimental group before using manipulative media and to determine the final ability to recognize the concept of numbers in children aged in the control group and in the experimental group after using manipulative media and to find out whether there is a significant effect on the ability to recognize number concepts in early childhood in the control group and the experimental group before and after being given manipulative media. The research method used was a quasi-experimental. This quasi-experimental method was carried out because the researchers did not form a random class with children, thus the experiment was carried out on existing classes. The results showed that there was a significant effect on the ability to recognize the concept of numbers in early childhood. This was proven from the average value of the experimental group pre-test 20.8000 changed to 37.7000 at the time of post-test, while in the control group pre-test 19.4545 changed to 22.6364 at the time of post-test. This proves that the manipulative media treatment applied can have an effect on the ability to recognize the concept of numbers for early childhood.
Financial Innovation and Performance of Deposit Money Banks in Nigeria

The research investigated the impact of financial innovation on the performance of Deposit Money Banks in Nigeria. A time series data covering the periods, 2009 – 2021 was used. The assets of the Deposit Money Banks was used as the dependent variables while the Point of sales, Automated Teller Machine, Internet Banking and Mobile Banking served as the independent variables. The researcher used the ARDL model in the analysis. Further, post-test analysis using serial autocorrelation, heteroskesdasticity, CUSUM were conducted. It was discovered that POS had the highest performance while internet banking had the least. More so, the variables are statistically significant. The authors recommended that internet accessibility should be made cheaper for easy access and usage. Banks should reduce the ownership cost of POS machine and rural banks should conduct periodic seminars and knowledge dissemination programmes for their rural customers so as to increase their knowledge of mobile banking activities
Use of Cloud Computing in Tutorial Group Discussions as a Method of Teaching and its Influence in Learning: A Case of Kenyatta University

Cloud computing has lately been deployed in educational institutions for a range of services and applications by way of virtualization of computing resources such as processors, memory, storage and networking.This paper discusses the use of cloud computing in tutorial group discussions as a teaching pedagogy and its influence in learning.It explores the essentials in tutorial group discussions in class including the preparations, activities, processes, benefits and opportunities.The research utilized a mixed method approach including qualitative and quantitative for data collection, then both action research and agile software development to develop and test the cloud computing based tutorial management system (Tutmas).Results indicated that both students and lecturers appreciated the benefits and usefulness of regular tutorials.Further, both students and lecturers appreciated the role of the cloud based Tutmas platform in improving efficiency in the management of tutorials and enhancement of students' learning experiences and academic skills.
Quantum dynamical decoupling by shaking the close environment

Quantum dynamical decoupling is a procedure to cancel the effective coupling between two systems by applying sequences of fast actuations, under which the coupling Hamiltonian averages out to leading order(s). One of its prominent uses is to drive a target system in such a way as to decouple it from a less protected one. The present manuscript investigates the dual strategy: acting on a noisy “environment” subsystem, such as to decouple it from a target system. The target system only has a Hamiltonian coupling to this close environment which itself undergoes Lindbladian decoherence. The potential advantages are that actions on the environment commute with system operations, and that imprecisions in the decoupling actuation are harmless to the target. We consider two versions of environment-side decoupling: adding an imprecise Hamiltonian drive which stirs the environment components; and, increasing the decoherence rates on the environment. The latter can be viewed as driving the environment with pure noise and our conclusions establish how, maybe counterintuitively, isolating the environment from noise sources as much as possible is often not the best option. In this setting, we can explicitly analyze the induced decoherence on the target system, and focusing on the example of a two-level environment subsystem, we establish how the induced decoherence is influenced by the parameters in both cases. The analysis combines Lindbladian derivation, adiabatic elimination, and Floquet modeling in a way that may be of independent interest.
CULTURE AS A DOMINANT IN THE CONTENT OF VOCATIONAL TRAINING FUTURE SPECIALISTS IN THE FIELD OF CULTURE

The article was characterized the essence of values in the contemporary philosophical and cultural, social and pedagogical, psychological and pedagogical paradigms, and educational methodological structures. Axiological principles were revealed as the extrapolation of values, that in addition to needs and interests are an important binding link between society and the individual. In the paper were allocated and characterized three groups of values of modern education so within the first were grouped values that are distinctive of those educational institutions that are translators of educational, social, and spiritual experience. The second group was assigned vital personal values for the future experts in the field of culture, which should be generated in the process of professional background in higher education institutions so that they can feel comfortable in their future professional activity. Whereas, in the third group we were assigned those values that should be leading currently in the formation of future experts in the field of culture as a socially active identity, citizen, and patriot. The work was described the current classification of values in the philosophical and cultural, psychological, and pedagogical contexts. It was given the characteristics of the leading personal values of future experts in the field of culture, which should be formed in the axiological surroundings in the higher education institutions from three positions: public value, social value, and personal value. It was proved that the priority granted to personal values, individually conscious attitudes future experts in the field of culture to the level and quality of the gained knowledge, established competencies.
SYSTEM OF INTELLECTUAL INTERACTION OF NON-GAME CHARACTERS BASED ON PERSONALITY CHARACTERISTICS

The work is devoted to the implementation of a library for intellectual interaction between non-player characters based on the characteristics of their personality. the following results were obtained: 1) the analysis of theoretical information was carried out, and the requirements for the system were identified; 2) the architecture of the system was designed, its database, algorithms, class diagram; 3) implemented algorithms for building relationships between characters using fuzzy logic; 4) the construction of a graph reflecting the social connections between the characters has been implemented.
Pengembangan Fitur Rekam Data Menggunakan Metode Multiple File (Studi Kasus Budidaya Jamur)

In the process of mushroom cultivation there are several things that must be considered, namely the factors that support the growth of mushrooms. The supporting factors that contribute to the growth of fungi are temperature conditions, humidity, air quality and light intensity. Based on the above conditions, this study aims to build a data recording feature to record the conditions of temperature, humidity, air quality and light intensity. With reference to the formulation of the problem, this study aims to develop a data record feature that later the data can be used by farmers to monitor the conditions of their cultivation sites or for further research. The method in this study using Multiple Files. The data recording feature will be carried out in real time using a Data Logger using Arduino Mega and additional sensors, including: DHT-22, MQ-135, LDR. From the resulting data retrieval, the feature has been running well. Data retrieval for 1 hour where data is taken every 10 minutes produces: The DHT-22 sensor shows an average temperature between 29.9 C and humidity of 74.8%. The LDR sensor shows a numerical value according to the light conditions in place, the brighter the light, the greater the value and vice versa. The MQ-135 sensor shows an average result of 117 PPM. This data record feature can help researchers and mushroom farmers who want to monitor the condition of their mushroom cultivation locations.
The Description of Grammatical Errors in the Eighth Grade Students’ Descriptive Text of SMP Negeri 2 Gunungsitoli Utara in 2021/2022.

Writing is one of the most difficult English skills. Therefore, many students who learn English as a foreign language tend to commit errors in their writings. Meanwhile, students are expected to be able to write English texts correctly. Based on the background of the problem, an analysis of grammatical errors is important to conduct. Therefore, the research was conducted with a qualitative descriptive design. It aims to describe the grammatical errors based on the types of omission, addition, misformation and misordering committed by the eighth grade students of SMP Negeri 2 Gunungsitoli Utara in 2021/2022. Furthermore, the data were collected from students’ descriptive writings. Finally, as the research findings, the type and percentage of grammatical errors committed by the students were omissions (58.82%), followed by misformation (29.41%), addition (8.82%), and misordering (2.94%). Thus, the results of this study can be used as a reference for students' writing problems at the school concerned.
Application of Training and Development to Improve the Performance of Teachers at SMP 5 Medan after the Pandemic

This Community Service activity aims to conduct training and development to improve teacher performance after the Covid-19 Pandemic. The location of service activities at the Negri 5 Junior High School (SMP) Medan. This activity uses the blanded learning method which is a learning method which combines two learning patterns, namely by utilizing technology and learning through teaching staff. This service activity went smoothly and all participants who were teachers were very enthusiastic in participating in the activity until it was completed. After the material was delivered, the participants were given the opportunity to ask questions and answers about the discussion of the material and even the obstacles experienced when learning was carried out after this Pandemic. There are 6 (six) models offered by the activity team that are considered capable of helping to improve Teacher performance, namely: 1) Using a Controlled Face-to-Face System Model. 2) Using a Rotation System Model. 3) Using a Flexing System Model. 4) Using an Online Turnaround System Model. 5) Using a Self-Combination System Model. 6) Using the Online Control System Model. Metode the delivery of blended learning material which is a perfect combination of face-to-face and also online, where during the Covid-19 Pandemic, it is known that there are many technologies that have undergone developments that greatly support the world of education in order to achieve success in learning. This blended learning method is also considered effective because it is to develop an innovation in the world of education in order to create optimal results.
Attribute-based keyword search across encrypted cloud data with multiple authorities

Searchable Encryption (SE) is a crucial technique for cloud data security and usability (SE). When employing Ciphertext Policy Attribute-Based Encryption (CP-ABE) and the Ciphertext Policy Attribute-Based Keyword Search (CP-ABKS), encryption and access control may be done simultaneously. A single attribute authority is responsible for authenticating user certificates and delivering secret keys in current CP-ABKS systems. A single point of failure results in distributed cloud systems. To address these difficulties, we explain in this paper the MABKS system, which is designed to help cloud devices with limited computing and storage capabilities. The MABKS system has added capability for tracing and modifying malicious attribute authority. According to our detailed security investigation, the MABKS system is safe in both selective-matrix and selective-attribute models. Our trials using real-world datasets have shown the MABKS system's efficiency and usefulness.
Mediatisasi Politik dan Perilaku Pemilih: Penggunaan Google Trend Sebagai Metode Alternatif Memprediksi Kemenangan Kandidat

In 2020, Indonesia has held simultaneous regional head elections which held governor elections in 9 provinces, mayor elections in 37 cities and regent elections in 224 regencies. Candidates in general elections often use surveys as a reference in estimating their potential for victory, even though to conduct popularity, acceptability and electability surveys require a high level of validity and reliability, for this reason, a large survey team is needed in distributing questionnaires and large funds for operational needs, so it is necessary to develop alternative methods of data collection in predicting candidate winner, other than survey methods. Google Trend as a service from Google that provides site search statistics by displaying the popularity of keyword within a certain time range where statistical results can be displayed by region has the potential to be an alternative method to surveys. In several previous studies in the United States and Canada, Google Trend managed to correctly predict the winner of the presidential election in the US and the winner of the election in Canada. This research fills a gap in the Indonesian context to see the accuracy of Google Trend in predicting the winner of the governor election in 9 provinces in the simultaneous regional elections in 2020. As a result, Google Trend succeeded in predicting the victory of candidates in Jambi, Central Kalimantan, North Sulawesi, West Sumatra; while doubtful in Bengkulu and South Kalimantan; and Google Trend is not accurate in predicting in North Kalimantan, Riau Islands and Central Sulawesi. Google Trend's lack of accuracy in predicting the governor's candidate victory in Indonesia is allegedly because Google Trend's services in Indonesia have not reached the district/district level compared to the United States and Canada which have reached the district/sub-district level. In addition, as an alternative method, there are a number of important notes for Google Trend because it has not been able to distinguish the tone of news whether it is positive or negative because Google Trend is still focused on the number of keyword searches that are most sought after by users, even though the tone of the news is important to see public sentiment.
Robust video steganography framework based on channel-space attention

In robust video steganography, a message is embedded into a video such that video distortions are avoided while producing a stego video of imperceptible difference from the cover video. Traditional techniques achieved robustness against particular distortions but are complicated in computation and design, and rely on different compression standards. Nowadays, deep-learning-based methods can achieve impressive visual quality and robustness to attacks. We propose a framework with a channel-space attention mechanism for robust video steganography. The framework is composed of depthwise separable convolution layers that can learn channel-space segments for embedding and extraction. The secret messages are distributed across channel-space scales to increase imperceptibility and robustness to distortions. This end-to-end solution is trained with the 3-player game approach to conducting robust steganography, where three networks compete. Two of these handle embedding and extraction operations, while the third network simulates attacks and detection from a steganalyst as an adversarial network. Comparative results versus recent research show that our method is more robust against compression and video distortion attacks. Peak signal-to-noise ratio and structural similarity index were used for evaluating visual quality and demonstrate the imperceptibility of our method.
Detection Malaria Base Microscope Digital Image with Convolutional Neural Network

Malaria is a tropical disease that infects human red blood cells caused by infection with the plasmodium parasite. Plasmodium parasites spread to humans through female Anopheles mosquitoes and can reproduce in human blood cells. Malaria is a health problem that is at risk of causing other health problems such as anemia and even death. The current gold standard for malaria diagnosis is laboratory diagnosis by microscopic examination to find the malaria parasite through the blood cells of the patient. However, the diagnosis of malaria through microscopic observation of blood cells has the potential to take a long time, because the plasmodium parasite has a very small size. The malaria detection system using the Convolutional Neural Network (CNN) method is designed to detect malaria in human blood cells. CNN is a machine learning method designed to classify objects in an image. The system was built in three stages of development, namely the development of a CNN model for malaria detection, software development and hardware development. The hardware components used in the system include Raspberry pi, Raspberry Pi camera module, and LCD. The results of the malaria detection test using the CNN model gave an accuracy of 98.76% which was tested on blood cell images from a microscope