Reviewerly/exHarmony huggingface dataset:
Enabling Eating Detection in a Free-living Environment: Integrative Engineering and Machine Learning Study

Monitoring eating is central to the care of many conditions such as diabetes, eating disorders, heart diseases, and dementia. However, automatic tracking of eating in a free-living environment remains a challenge because of the lack of a mature system and large-scale, reliable training set.This study aims to fill in this gap by an integrative engineering and machine learning effort and conducting a large-scale study in terms of monitoring hours on wearable-based eating detection.This prospective, longitudinal, passively collected study, covering 3828 hours of records, was made possible by programming a digital system that streams diary, accelerometer, and gyroscope data from Apple Watches to iPhones and then transfers the data to the cloud.On the basis of this data collection, we developed deep learning models leveraging spatial and time augmentation and inferring eating at an area under the curve (AUC) of 0.825 within 5 minutes in the general population. In addition, the longitudinal follow-up of the study design encouraged us to develop personalized models that detect eating behavior at an AUC of 0.872. When aggregated to individual meals, the AUC is 0.951. We then prospectively collected an independent validation cohort in a different season of the year and validated the robustness of the models (0.941 for meal-level aggregation).The accuracy of this model and the data streaming platform promises immediate deployment for monitoring eating in applications such as diabetic integrative care.
Research on Time-of-use Price Trading of Distributed Power Based on Blockchain

With the advancement of electricity marketization, distributed power trading and time-of-use price mechanism have become important directions. A distributed power time-of-use trading model based on blockchain technology is designed in this paper, and the trading process is introduced in detail. The transaction is established in an annual cycle and settled monthly. The trading model adopts the average price of the seller’s and buyer’s quotation price by matchmaking. A reasonable default compensation mechanism has been designed, which is conducive to promoting buyers and sellers to publish accurate and reasonable purchase and sale information as far as possible. A distributed power time-of-use trading system based on blockchain technology is developed on the Ethereum platform. Combined with a distributed power time-of-use trading example, the feasibility of the trading system for distributed power time-of-use trading is verified. The distributed power time-of-use trading system can achieve fair and equal trading between distributed generations and power customers, and improve the trading efficiency of distributed power, which enhances the enthusiasm of all parties to participate in transactions and the local consumption of distributed renewable energy.
An Adjacency matrix-based Multiple Fuzzy Frequent Itemsets mining (AMFFI) technique

Recently, discovering helpful information from a database consisting of transactions has been a critical research topic.Several frequent itemsets mining for association rule mining, algorithms that can only handle binary databases have proposed.Transactions using numerical values, on the other hand, are ubiquitous in real-world applications.Thus, with reference to the quantitative transactional database, several algorithms were developed and "fuzzy frequent itemsets" (FFI) were discovered.Most of them just consider the term having maximum cardinality.As a result, the number of fuzzy regions processed is equal to the number of original elements.Multiple fuzzy zones of an item, on the other hand, give a better result for making a correct decision.This study presents an AMFFI-miner (Adjacency matrix-based Multiple Fuzzy Frequent Itemsets) for discovering multiple FFIs out of a quantitative transactional database.An adjacency matrix and fuzzy-list structure were designed to find multiple FFIs by scanning database only once and generates less number of candidate itemsets.Join two nodes if its co-occurrence between two fuzzy linguistics terms satisfies minimum support threshold by finding the co-occurrence between two fuzzy linguistics terms directly from the adjacency matrix, thus reducing the number of nodes joining and speeding up discovering multiple FFI.Experiments carried out to compare the suggested method's performance to that of existing methodologies based on running time, memory utilization, and the number of nodes joining.
Lightweight Cryptography for Resource Constrained Devices

Counterfeiting of products is constantly increasing, and more so in developing markets. As per the statistics counterfeiting incidents have increased over 24% in India in the year 2019 over previous years and badly hitting the economy by creating an over of 1-lakh-crore hole in the economy. These counterfeited products pose a threat to the sales figures of Original Equipment Manufacturers (OEM)’s and the safety of end-users. Problem of counterfeiting can be reduced substantially by providing a means for distinguishing the original product from the duplicates circulating in the market. Cryptography is a method used to encrypt certain data that can only be read in its original form by the person for whom the information is intended by the method of decryption. In order to fit these cryptographic algorithms into the products, the cryptographic primitives deployed on these devices have to be lightweight so as to not increase the size of the end product. In this paper, we have discussed and validated a method of authenticating original products. Low-cost methods of cryptography are required for the widespread adoption of anti-counterfeiting measures. Hummingbird-2 is a robust lightweight cryptographic algorithm that strikes a good balance between cost and security and it has comparatively a smaller footprint when compared to other light weight cryptographic algorithms. This paper, details the design and validation of Hummingbird-2 a light weight cryptographic algorithm and its implementation using Verilog in Xilinx ISE. The algorithm is of tested for an input which is the plain text and key size of 128bits respectively. The validated functional design is implemented on the target platform SPARTAN 6. A comparison between Hummingbird-2 and Present-80 algorithms is also presented. Quantitative analysis of timing analysis, slice utilization, macro statistics and memory usage of algorithm is tabulated and presented .
Test Processing of Seismic Monitoring using Sleipner 4D Data

To verify the potential of time-lapse seismic monitoring for the Korean domestic CCS project, we analyzed the Sleipner project dataset.Time-lapse data used for input consists of the baseline and monitoring data that were recorded in 1994 and 2008, respectively.The difference between the baseline and the monitoring data, subsurface CO 2 plume, was clearly identified as a change in the seismic amplitude.To investigate the effect of time-lapse processing, we tested input parameters of time-varying amplitude gain and lateral smoothing modules.The result shows that gain should be applied on the entire traces, and smoothing can increase the signal to noise ratio.By comparing the derived amplitude difference with the results of the previous paper in the Sleipner project, it was confirmed that the CO 2 plume region was consistent.The accumulated experience of the Sleipner data will be embodied in a best framework to assist future CO 2 storage project in Korea.
How Does Analysis of Handwritten Notes Provide Better Insights for Learning Behavior?

Handwritten notes are one important component of students’ learning process, which is used to record what they have learned in class or tease out knowledge after class for reflection and further strengthen the learning effect. It also helps a lot during review. We hope to divide handwritten notes (Japanese) into different parts, such as text, mathematical expressions, charts, etc., and quantify them to evaluate the condition of the notes and compare them among students. At the same time, data on students’ learning behaviors in the course are collected through the online education platform, such as the use time of textbook and attendance, as well as the scores of the online quiz and course grade. In this paper, the analysis of the relationship between the segmentation results of handwritten notes and learning behavior are reported, as well as the research on automatic page segmentation based on deep learning.
Emerging Role of Block Chain Technology in Maintaining the Privacy of Covid-19 Public Health Record

Amid covid-19 outbreak contact tracing becomes an essential tool for protection and connecting to the most possible connections with the infectious patients which leads to addition of high number of individuals data to be recorded in the location wise public health system. The public health records surface complications concerning security, reliability and supervision. Here the novel technology Blockchain is on the track to take over a part to battle the Covid-19 and overthrow the pandemic. This technology affords a solution for the adjustment between public health and private record. Now as blockchain technology has entered the area of possible units against covid-19.The blockchain technology protected anonymized private data in the search for record generated by the pandemic. The utmost valued defence against the Covid-19 is information which includes one’s contacts and connections as well path tracing. However, it derives at a great cost of private data. A blockchain-based covid-19 infected-patient data record would guarantee untampered neutral data with cryptographically secured decentralized data record protection. Moreover, the blockchain acquired knowledge confirms that data records are only used for its planned purpose, as it authenticates the applied algorithms. Additionally, block chain and secure hardware together can eliminate hacks and annoying data-selling. Hence, block chain can efficiently provide data privacy. This paper in the context of block chain introduces Gravity: a distributed protocol for handling the amount of all potential data and the usage of distributed data provisions such as IPFS is inevitable in this charge. Medical-Chain platform for protecting the identities of Covid infected and traced patients and their history. One must be sure of data records to be following HIPPA compliance to use anonymous nature and proper encryption.
Enabling and Enforcing Social Distancing Measures at Smart Parking Infrastructures Using Blockchain Technology in COVID-19

Social distancing is the only preventive nonpharmaceutical measure to limit the transmission of Coronavirus disease (covid-19). By reducing the closeness and frequency of human physical gatherings and maintaining adequate distance among the individuals can minimize the spread of disease. IoT and AI revolutionary domain technologies can support in implementing and measuring minimum social distancing set-up by using the smart application. Though Smart City and Smart parking System are innovative, smart, and safe practices for end-users, the outbreaks of COVID-19 have revealed limitations of the existing deployments. Hence timely enforcements, framework, and architecture have to be developed accordingly. The focus of this paper is to provide a practical model where emerging blockchain technology is enforcing in finding many solutions and dealing with the problems in maintaining social distancing at smart parking in such pandemic outbreaks. The research aims to provide an architecture and framework in developing such applications using for smart parking concerning important issues and challenges of privacy and cybersecurity in implementing social distancing in practice.
İki Ayaklı Robot Kontrolü için DDPG ve PPO Algoritmalarının Değerlendirilmesi

Legged robots are very popular topics in the robotic field owing to walking on hard terrain. In the current study, the walking of a bipedal robot that is legged robot was aimed. For this purpose, the system was examined and an artificial neural network was designed. After, the neural network was trained by using the Deep Deterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO) algorithms. After the training process, the PPO algorithm was formed better training performance than the DDPG algorithm. Also, the optimal noise standard deviation of the PPO algorithm was investigated. The results were shown that the best results were obtained by using 0.50. The system was tested by utilizing the artificial neural networks that trained the PPO algorithm which has got 0.50 noise standard deviation. According to the test result, the total reward was calculated as 274.334 and the walking task was achieved by purposed structure. As a result, the current study has formed the basis for controlling a bipedal robot and the PPO noise standard deviation selection.
A neuroevolutionary method for knowledge space construction

In this paper we propose a novel method for the construction of knowledge spaces based on neuroevolution. The main advantage of the proposed approach is that it is more suitable for constructing large knowledge spaces than other traditional data-driven methods. The core idea of the method is that if knowledge states are considered as neurons in a neural network, the optimal topology of such a neural network is also the optimal knowledge space. To apply the neuroevolutionary method, a set of analogies between knowledge spaces and neural networks was established and described in this paper. This approach is evaluated in comparison with the minimized and corrected inductive item tree analysis, de facto standard algorithm for the data-driven knowledge space construction, and the comparison confirms the assumptions.
Media Strategy of the University: Connection between Aims, Structural Solutions and Activity

The case presents the experience of Ural Federal University in forming a strategy for its communication with target groups as a response to external challenges defined by new positioning of the institution in the international scientific and educational sphere. The university had to ultimately restructure the services responsible for promoting its brand, expand the variety of interaction practices both within the academic environment and from the outside – with graduates, their parents, teachers, scientific community, governance and business. Formation of the new media communication strategy has taken place under lack of appropriate management practices and their analytical support. These practices are still sketchy and remain underexplored. The case of Ural Federal University allows to supplement the arsenal of works and researches in the sphere of media strategies development of Russian universities. The novelty of the proposed case lies in analysis of the setting of strategic aims in this sphere, in developing and substantiation of structural decisions and practical mechanisms, which are confirmed by the verification of independent expert sources for effectiveness and realizability. The experience of Ural Federal University would be useful both to researchers of universities’ media activity and to practicians, because it reflects the experience if creating an effective model of modern media strategy, which allowed the university in 2021 to become one of the leaders of Russian Ministry of Education rating of Russian universities’ media activity.
Pengaruh Pasang Surut Terhadap Profil Muka Air Banjir Bantaran Sungai Musi Kota Palembang

Palembang is famous for the Musi River with a length of 460 km. The area along the river border is vulnerable to the impact of floods, this is the same thing that occurs in the Gandus District, which is mostly located on the banks of the Musi River. Analysis of the flood water level profile along the banks of the Musi River, which is affected by the tides is needed to find out how big the impact of flooding in the study area is. It aims to determine the type of tide, the amount of flow discharge, and simulation of river water level. Primary data was taken using a current meter to determine the speed of river flow. Some of the secondary data needed are river channel topographic data, rainfall data for 10 years, channel cross-section data and Musi River level data. The analysis was carried out in the form of a simulation of flood water level with the help of the HEC-RAS 5.0.7 program. The tidal type of the Musi River is based on the results of the study in the form of Diurnal Tide, with a watershed area of 2.2 km2 in Gandus District, the runoff discharge is 280.34 m3/s. The simulation results with the help of the HEC-RAS program obtained an average runoff height on the Musi River border of 2.7 m. The high runoff from the simulation can have a negative impact on the surrounding community, so there needs to be serious handling for flood control.
Cognitive sensor systems for NDE 4.0: Technology, AI embedding, validation and qualification

Abstract Cognitive sensor systems (CSS) determine the future of inspection and monitoring systems for the nondestructive evaluation (NDE) of material states and their properties and key enabler of NDE 4.0 activities. CSS generate a complete NDE 4.0 data and information ecosystem, i. e. they are part of the materials data space and they are integrated in the concepts of Industry 4.0 (I4.0). Thus, they are elements of the Industrial Internet of Things (IIoT) and of the required interfaces. Applied Artificial Intelligence (AI) is a key element for the development of cognitive NDE 4.0 sensor systems. On the one side, AI can be embedded in the sensor’s microelectronics (e. g. neuromorphic hardware architectures) and on the other side, applied AI is essential for software modules in order to produce end-user-information by fusing multi-mode sensor data and measurements. Besides of applied AI, trusted AI also plays an important role in CSS, as it is able to provide reliable and trustworthy data evaluation decisions for the end user. For this recently rapidly growing demand of performant and reliable CSS, specific requirements have to be fulfilled for validation and qualification of their correct function. The concept for quality assurance of NDE 4.0 sensor and inspection systems has to cover all of the functional sub-systems, i. e. data acquisition, data processing, data evaluation and data transfer, etc. Approaches to these objectives are presented in this paper after giving an overview on the most important elements of CSS for NDE 4.0 applications. Reliable and safe microelectronics is a further issue in the qualification process for CSS.
To Study the Role of Parents in Developing Computer Awareness among Schools Going Children in Pune City

The National Policy on Education 1986, as modified in 1992, stressed the need to employ educational technology to improve the quality of education. The policy statement led to two major centrally sponsored schemes, namely, Educational Technology (ET) and Computer Literacy and Studies in Schools (CLASS) paving the way for a more comprehensive centrally sponsored scheme – Information and Communication Technology @ Schools in 2004.The comprehensive choice of ICT for holistic development of education can be built only on a sound policy. The initiative of ICT Policy in School Education is inspired by the tremendous potential of ICT for enhancing outreach and improving quality of education. A parent is primary helper, monitor coordinator, observes, record keeper and decision maker for the child. The most important duty of parents is to look after their children and to take interest in their welfare and progress. ” Successful parenting entails both effective components in terms of commitment, empathy and positive regard for children.”
Construction of Community Medical Communication Service and Rehabilitation Model for Elderly Patients under the Internet of Things

The objective of this study was to discuss the health management of elderly patients in the community and the management of community rehabilitation under the support of the new Internet of Things (IoT). The IoT technology was adopted to monitor the wearable devices through mobile medical physiological data. The heart rate, blood pressure, respiratory rate, and other physiological indicators of the elderly were collected in real time. The support vector machine (SVM) algorithm was selected as the core algorithm for the elderly physiological index disease risk assessment, the fuzzy comprehensive evaluation method was adopted as the core method of the elderly disease risk quantitative assessment model to process the physiological indicators, and finally, a reasonable physiological index processing model and quantitative indicators of disease risk were obtained. The data on vascular disease were selected from the MIMIC database. In addition, the advantages and disadvantages of the SVM algorithm and the Backpropagation Neural Network (BPNN) algorithm were compared and analysed. The final verification results showed that the fusion accuracy of the SVM processing MIMIC database and the University of California Irvine (UCI) dataset was 0.8327 and 0.8045, respectively, while the fusion accuracy of the BPNN algorithm in processing the same data was 0.7792 and 0.7288, respectively. It was obvious that the fusion accuracy of the SVM algorithm was higher than that of the BPNN algorithm, and the accuracy difference of the SVM algorithm was lower than that of the BPNN algorithm in different groups of data. In the verification of the elderly disease risk quantitative assessment model, the results were consistent with the selected data, which verified the effectiveness of the design model in this study. Therefore, it can be used as a quantitative assessment model of general elderly physiological indicators of disease risk and can be applied to the community medical communication management system. It proved that the model of medical communication and rehabilitation services for elderly patients in the community constructed in this study can definitely help the development of community service for the elderly.
Development "Acep the Explorer" an Educational Game Introduction Animal Ecosystem

Acep the Explorer is a game about animal ecosystem, this lesson is a must-know lesson for elementary school children who aim to introduce to children who are just beginning to know animals that are foreign to them, so that children can recognize the animals and place them according to their own ecology.Educational games are games designed or created to stimulate thought including increasing concentration and problem solving.This game developed by multimedia development system, start form concept, design, until testing.AI development is also carried out in this game, we use a finite state automata to create enemies that annoy players The result of this research, an educational game about animal ecosystem and the children's response to the use of educational games know animals according to ecosystem is good, children can get to know animals according to their ecosystem.And the AI, the Finite state automata in this game makes the game more challenging, interesting and fun.Because in this game they can learn while playing games and face some obstacles and destroy the enemy hunting device and complete the game.This research contributes to combining AI technology with educational games to make students learning about animal ecosystems more interesting and challenging
FEATURES OF THE GEOLOGICAL STRUCTURE AND SEISMOTECTONICS AT THE STS AREA BY EARTH REMOTE SENSING DATA

The general methodological approach on using specialized remote sensing data of the Earth (ERS) on the territory of the Semipalatinsk Test Site area to identify active faults and seismogenic structures potentially dangerous with respect to the occurrence of earthquakes is presented. Specialized ERS data include data from aerogeophysical surveys, satellite multispectral, radar and gravimetric surveys. Examples of different ERS data application to identify active faults and areas with excess mass of the geological environment associated with the seismicity at the studied area are shown.