Reviewerly/exHarmony huggingface dataset:
Stock Price Prediction using Prophet Facebook Algorithm for BBCA and TLKM

Stocks are an investment instrument that is starting to be in great demand by the public today. However, stock prices are fluctuating, making people feel doubts about when they are going to invest. To overcome these doubts, we need a way to predict stock prices. This study aims to predict stock price fluctuations using Facebook's Prophet Algorithm to help people decide their investment in stock. The research object used is BBCA and TLKM stock price data in the form of a time series from 03 May 2021 to 28 April 2022 with stock price testing data for the next week, namely 01 May 2022 to 07 May 2022. From the training and testing process done, a prediction is produced that is very close to the original value. Using the RMSE, MSE and MAE measurements, we get RMSE 49.6, MSE 2462.1 and MAE 37.5 for BBCA and RMSE stocks, namely 21.3, MSE 456.5 and MAE 19.2 for TLKM shares. The conclusion is that Facebook's Prophet Algorithm is suitable for predicting stock prices.
Road Traffic Small Target Detection Algorithm Based on Lightweight YOLO V5

For the current problem of large number of parameters and large computation of the target detection network, the lightness improvement is carried out on the basis of yolov5s, and the dynamic convolution of OD is introduced to compensate for the accuracy degradation caused by the lightness, and the Map0.5 of the improved Yolov5s- EfficientNetV2-OD network reaches 82.8 through comparison experiments, and the FPS is 59 frames per second. The experiment proves that the network maintains the detection accuracy and detection speed of the network for small targets on the basis of light weighting. It is suitable for deployment on vehicle-mounted devices.
Heterogeneous Fault-Tolerant Aggregate Signcryption with Equality Test for Vehicular Sensor Networks

The vehicular sensor network (VSN) is an important part of intelligent transportation, which is used for real-time detection and operation control of vehicles and real-time transmission of data and information. In the environment of VSN, massive private data generated by vehicles are transmitted in open channels and used by other vehicle users, so it is crucial to maintain high transmission efficiency and high confidentiality of data. To deal with this problem, in this paper, we propose a heterogeneous fault-tolerant aggregate signcryption scheme with an equality test (HFTAS-ET). The scheme combines fault-tolerant and aggregate signcryption, which not only makes up for the deficiency of low security of aggregate signature, but also makes up for the deficiency that aggregate signcryption cannot tolerate invalid signature. The scheme supports one verification pass when all signcryptions are valid, and it supports unbounded aggregation when the total number of signcryptions grows dynamically. In addition, this scheme supports heterogeneous equality test, and realizes the access control of private data in different cryptographic environments, so as to achieve flexibility in the application of our scheme and realize the function of quick search of plaintext or ciphertext. Then, the security of HFTAS-ET is demonstrated by strict theoretical analysis. Finally, we conduct strict and standardized experimental operation and performance evaluation, which shows that the scheme has better performance.
Decentralized Interaction With Elder-Care Tasks

The rate of ageing in the world is extremely fast. Elderly people are becoming more and more prevalent all over the world. The need for services like hospitals, retirement communities, and assisted living facilities has increased as the proportion of older persons in the population rises. For legislators, medical experts, and society as a whole, recovering command over this populace's assumptions will introduce new difficulties. With the aid of smart robot technology, older people can live independent, satisfying lives while remaining safe and secure. As a result, the most notable aspect of this paper is an occy robot. An AI model is used to process facial recognition and speech data, and occy assists elderly people with daily tasks. Occy is more effective for home appliances and healthcare systems. Occy may do tasks like equipment control in the home, safety, and wellbeing sensing, travelling outside, emergency situations and everyday other tasks.
Medical intelligent system application in schizophrenia treatment satisfaction based on neural networks

Aiming at the problems of poor convergence and uneven energy consumption of heed routing protocol, this paper proposes an opfh routing protocol based on options clustering algorithm for the first time. Firstly, the protocol divides the network into several top-level groups by using options clustering algorithm, then elects each top-level group at the same time, and designs and develops the medical intelligent system through wireless sensor. The medical intelligent system developed in this paper not only facilitates the completion of rehabilitation work, but also facilitates the management of hospital rehabilitation, greatly reducing manpower and material resources. It plays an important role in the treatment of schizophrenia. However, patients with schizophrenia have high inner sensitivity, often accompanied by anxiety and depression. Nurses need to take the initiative to communicate with patients, understand and meet the needs of patients, and use the system to manage the negative emotions of patients, so as to improve the confidence and compliance of patients. In order to improve the treatment satisfaction of patients, the large-scale data set without marked target task was used for domain transfer learning of small-scale data set. In this paper, the research of wireless sensor is applied to medical intelligent system, so as to promote the vigorous development of medical intelligent system.
Real-time NIR camera brightness control using face detection

The face image analysis field is a well-established research area in computer vision and image processing. An important requirement for accurate face image analysis is a high-quality input face image. In different real-life scenarios, however, the face is often not properly illuminated, which makes the face analysis very difficult or impossible to accomplish. Although a better performance is obtained by changing the spectrum from visible to near-infrared, it is still not enough for extreme illumination conditions. To obtain a high-quality near-infrared face image, a fast automatic brightness control method using approximate face region detection is proposed, which properly adjusts the brightness of the face part of the image. A novel algorithm for approximate face region detection based on spatio-temporal sampled skin detection is proposed together with the split-range feedback controller and the face absence handle. The proposed method is much faster than state-of-the-art solutions and accurate in approximate face region detection. The complete execution time is lower than 10 milliseconds which makes it suitable for hard real-time embedded system implementation and usage, while the reference brightness value is achieved within 10–15 frames, making it robust to extreme illumination conditions in a scene.
Software Effort Estimation Based on Use Case Reuse (Back Propagation)

Abstract: Project managers often use effort estimating strategies to manage the human resources of current or upcoming software projects. Prior to project implementation, cost, time, andpersonnel estimation are basically necessary. For every project of software, getting accuracy in Effort Estimation has always been difficult. In this study, the estimation of software development effort was determined using a back propagation model. This model's goal is to investigate the capabilities and potential uses of Utilizing artificial neural networks (ANN) as a tool for forecasting the effort required for software development. In order to estimate the software work, we are attempting to implement a machine learning technique in this research. Out of all machine learning methods, we are applying an algorithm based on Artificial Neural Networks that is Back propagation. The Desharnais dataset, a well-known publicly available dataset for estimation of software effort, is used to test the approach. The performance and accuracy of the tested model have been evaluated using three metrics: MMRE, MRE, and Pred (0.25). In the sections below that follows, I explain the algorithm and its results
Improved Meanshift Tracking Algorithm Based on Optical flow

The mean shift tracker has difficulty in tracking fast moving targets and suffers from local optimal problem. To overcome the limitation of the mean-shift tracking algorithm, a new approach is proposed by integrating the mean-shift algorithm and optical flow methods. Even with n the rough position, the mean-shift algorithm achieves precise tracking of the target. Several tracking experiments show that the proposed algorithm can effectively track fast moving target and overcome the tracking error cumulating problems.
A novel approach for E-Government services with artificial intelligence using CNN

Artificial Intelligence (AI) is a domain that works on various complex applications, such as E-government services. In order to provide government services to the people, an online AI-based Deep Learning (DL) model has been developed to check the availability of government schemes. However, several E-government services are not available to the citizens based on their usage. Many challenges have been identified while using E-Government services. This paper introduces the DL model, Convolutional Neural Networks (CNN), to solve the issues in E-Government services. The system focuses on maintaining E-government data resources, and CNN is primarily used to automate E-Government services. Finally, CNN has developed an innovative E-Government environment to support the design, development, and implementation of applications.
CS-Jaya: Hybridization of Cuckoo and Jaya Algorithm

Jaya algorithm and Cuckoo Search Algorithm are newly developed metaheuristic algorithms for solving optimization algorithms. In this paper, we have proposed a new algorithm, hybridizing the concept of the Jaya algorithm with the Cuckoo Search Algorithm, named CS-Jaya. CS-Jaya algorithm utilizes the advantage of both algorithms. Five test functions are selected from the literature for testing of CS-Jaya performance. Then, lastly, numerical results reveal the efficiency of the presented algorithm.
A Comparitive Study on Abstractive Text Summarization Techniques Using Deep Learning (ATS-DL)

The amount of textual data has significantly expanded in recent years, thereby facilitating easier information extraction and analysis. To retrieve useful knowledge within a reasonable time period, this information must be summarized. Though lot of work has already been done in abstractive text summarization (ATS), exploring ATS using deep learning techniques still has some major issues and lot of research openings. Hence, deep learning-based approaches have been used for implementing ATS. We have built ATS model using LSTM, BiLSTM, GRU and transformers along with attention mechanisms embedded in it. Gigaword dataset was used for training and comparing the models. Our system is compared using standard benchmarking systems like ROUGE-1, ROUGE-2 and ROUGE-L. Among these models, the pre-trained T5 model had a better abstractive summary at the same time, improving the ROUGE score by 9%.
A Study on the Context Mode of the Linking Verb “Shì” Addressing a New Branch Naming

Complementing the missing elements in Chinese clauses contributes to computer understanding. Based on the naming-telling structure and Clause Complex Theory, the paper explores the effect of the context when the former clause is a “shì"-clause and the latter lacks naming. The object of “shì" in the former clause as the missing naming of the latter clause is called the new branch naming. The object as a new branch naming introduced by “shì” is determined by the intelligibility and the referential specificity of the object. The context mode contains literal features and semantic features for resolution.
Research on Verb Expression Error Detection Algorithm in College English Writing Based on Fonnal Model

Based on the form language structure, the chain grammar formal structure framework is used to realize the form of sentences and reconstruct the management system of the chain grammar sentence dictionary. It aims to establish an excellent error detection system in English sentences for Chinese learners. The test data shows that the recall rate of wrong sentence detection increases by 4.5% compared with the original dictionary, and the accuracy rate also increases by 15.7%. The learners' accurate parsing of sentence patterns is improved by 12.2%. The research results show that the linguistic basic verb pattern syntax and structural model chain syntax adopted in this study can be applied to the research and construction of the error detection system of the written English verb type for Chinese learners.
Role of Blockchain Technology in the Financial Market

Purpose: Cryptocurrency technology has improved fast in the social economy and growth. Because cryptocurrency has many good qualities, it is initially employed for Bitcoin transactions.Methodology: With the advent of Bitcoin, the link between distributed ledger technology (DLT) and the banking market has become stronger and more integrated. As more banking institutions understood the relevance of DLT, they began experimenting with using it in financial activities, such as R3CEV, Hyperledger, and Qiwi.Findings: Many commercial organisations are beginning to experiment with DLT to reduce transactional costs and boost operational effectiveness, particularly in financial notes, cross-border payments, and asset-backed financing.Practical Implications: DLT has many potential applications in banking domains in the upcoming years.
Artificial Intelligence for Financial Services

It is difficult to argue against the fact that research has focussed on artificial intelligence (AI) and robotisation over the past few decades. Additionally, during the past several years, it has taken off and is now extensively used in numerous businesses across various industries. Most of the time, AI has been associated with some industrial sector process automation. Still, recently, the authors have noticed more positive technology uses, especially in the financial services industry. Due to several factors, the financial sector needs to adopt AI and recognise its potential. The industry has historically been concerned about unpredictability, legislation, stronger cybersecurity, technological limitations and disruption of established lucrative operations.Never before has there been more discussion about AI due to the advantages it provides to businesses that are providing financial services. That may explain why this change is referred to as the fourth industrial revolution. Both positively and negatively, it is quite disruptive. The effectiveness, accuracy and cost-effectiveness of solutions greatly increase. However, immense power also entails great responsibility.Precautions and security are more crucial than ever for businesses since the financial sector is changing significantly and quickly. The various benefits and drawbacks of this technology are yet unknown to humans. Although AI was first shown to us in the 1950s, it has recently gained new prominence as processing power, and the available quantity of data has increased dramatically.
A Study on Lyeohanshibgamuncho

At the early years of the 20th century, Lyeohanshibgamuncho was published twice at Nantong Hanmolin bookstore in China. It was Wang Seongsun who completed the compiling of Lyeohanshibgamuncho, instead of Kim Taeyeong. At the beginning, Muncho was edited by Kim Taeyeong. After three editions, 9 Korean litterateurs' works were picked up into a book, called Lyeohanmunseon or Lyeohangugamuncho. Afterwards, Wang Seongsun made Kim the 10th person and re-selected the works. Compared with Kim's Lyeohangugamuncho, the works selected by Wang were totally different. However, Kim and Wang followed the same principle when selecting.&#x0D; The birth of Muncho was closely bound up with historical background. Under the extremely serious national crisis, the Korean patriots, like Kim and Wang, realized the importance of national culture. They restated the history of Korean and promoted the literature of Korean. In that way, they tried to save the nation from subjugation and ensure its survival.
Path-planning and obstacle avoidance algorithms for UAVs: a systematic literature review

Recent interest in unmanned aerial vehicles (UAVs) has grown due to the wide range of possible civilian uses for these aircraft. However, present robot navigation technologies still need to be improved in various situations. Researchers are particularly interested in the 'Sense and Avoid' capacity as a critical issue. UAVs operating in civilian areas must have this functionality to do so safely. Numerous path planning and navigation algorithms have been developed for autonomous decision-making and control of UAVs. These path-planning algorithms are divided into either heuristic and non-heuristic or accurate methods. Both existing UAV route planning algorithms for the first and second techniques will be thoroughly compared in this work. Each algorithm is put through its paces in three diverse obstacle scenarios. Each method has been evaluated under various global and local obstacle information availability conditions while comparing the computational time and solution optimality.
Research on early warning system of provincial energy and power security based on extension cloud

Under the background of "carbon peak and carbon neutrality" and the comprehensive marketization of gas-fired power units, provincial energy and power security is facing a new situation. The corresponding supporting mechanisms and rules are immature, resulting in a large number of risk factors. Therefore, in order to ensure the security of provincial energy and power, the research and monitoring of risk factors are essential. This paper starts from the upstream, midstream and downstream links of the energy and power industry chain and analyzes the risk factors of energy and power security in terms of energy supply, energy utilization, unit operation, environmental impact, power system operation, and electricity market. Then, according to different risk sources, a dynamic risk early warning system suitable for provincial energy and power security is designed to monitor the security risks of the entire energy and power supply chain. In addition, combined with matter-element extension theory and cloud model, an extension cloud model suitable for energy and power security risk early warning is designed, from which the risk early warning level of provincial energy and power security can be obtained. Finally, the effectiveness of the above method for reflecting the risk situation of provincial energy and power security is demonstrated by arithmetic examples.
Digital intelligent distribution network fault risk early warning system based on data mining

Aiming at the problems of low fault warning efficiency and failure to transmit warning information in time in China's distribution network, a digital smart distribution network fault risk warning system based on data mining is proposed. The Oracle database is used to call and manage the data of the distribution network as a whole, and then the data mining method is used to collect and process the fault data. On this basis, the fault early warning model is established to realize the timely early warning of distribution network fault risk. Finally, the conventional fault risk early warning system is selected as the comparison system for the comparison test. The test results show that the fault risk early warning system of the distribution network based on data mining is superior to the conventional system in data transmission delay, which is conducive to improving the early warning efficiency and providing a guarantee for the safe and stable operation of distribution network.
The Fast and the Curious

Scientists get up to speed on phenomena ranging from fast moving atmospheric plasmas to a quickening glacial pace on Earth.
Wearable and Invisible Sensor Design for Eye-Motion Monitoring Based on Ferrofluid and Electromagnetic Sensing Technologies

For many human body diseases, treatments in the early stages are more efficient and safer than those in the later stages; therefore, detecting the early symptoms of a disease is crucial. One of the most significant early indicators for diseases is bio-mechanical motion. This paper provides a unique way of monitoring bio-mechanical eye motion based on electromagnetic sensing technology and a ferro-magnetic material, ferrofluid. The proposed monitoring method has the advantages of being inexpensive, non-invasive, sensor-invisible and extremely effective. Most of the medical devices are cumbersome and bulky, which makes them hard to apply for daily monitoring. However, the proposed eye-motion monitoring method is designed based on ferrofluid eye make-up and invisible sensors embedded inside the frame of glasses such that the system is wearable for daily monitoring. In addition, it has no influence on the appearance of the patient, which is beneficial for the mental health of some patients who do not want to attract public attention during treatment. The sensor responses are modelled using finite element simulation models, and wearable sensor systems are created. The designed frame of the glasses is manufactured based on 3-D printing technology. Experiments are conducted to monitor eye bio-mechanical motions, such as the frequency of eye blinking. Both the quick blinking behaviour with an overall frequency of around 1.1 Hz and the slow blinking behaviour with an overall frequency of around 0.4 Hz can be observed through experimentation. Simulations and measurements results show that the proposed sensor design can be employed for bio-mechanical eye-motion monitoring. In addition, the proposed system has the advantages of invisible sensor set-up and will not affect the appearance of the patient, which is not only convenient for the daily life of the patient but also beneficial for mental health.
A Novel Copy–Move Forgery Detection Algorithm via Gradient-Hash Matching and Simplified Cluster-Based Filtering

Copy–move forgery is one of the most frequently used methods for producing fake digital images. Current algorithms for copy–move forgery detection (CMFD) cannot combine high accuracy and fast speed. Motivated by the observation, we propose a novel CMFD algorithm whose workflow is as follows. First, we use a keypoint-extraction method with the lowest contrast threshold to extract more keypoints from the input image. Second, a new technique, gradient-hash matching, finds pairs of similar keypoints quickly and effectively using a hash table, where the hash value is computed using gradients of keypoints. Subsequently, a new method called simplified cluster-based filtering exploits the density pattern of keypoints in the copy–move regions to remove false matching keypoint pairs. Finally, image matting is applied to indicate the forgery regions vividly. Extensive experiments show that not only the new algorithm is better than the state-of-the-art algorithms in terms of computation correctness, but also its computation time is drastically less. Commonly only about half time is needed. The relative time saving is even higher when images are larger. Different algorithms modules are compared through experiments to choose the best combination.