Reviewerly/exHarmony huggingface dataset:
Night Vision Thermal sensor based Animal Movement Observation using CNN and YOLO v3

Developing an automated system for wildlife detection and recognition using thermal cameras can be challenging nowadays, which have many applications in wildlife conservation and management. This includes many challenges like detecting false positives and negatives and also environmental factors like temperature and humidity. Developed a system for the classification of animals using one of the developed algorithms, the computer-aided CNN algorithm and Python Flask web application that loads a pre-trained convolutional neural network (CNN) model for image classification (cat or dog) and allows the user to upload an image for classification. The wildlife conservationists need a system, which uses a thermal images or videos and detects the animal presence in video. Temperature difference is used to distinguish the animals and can develop the system which uses images to detect the animals in a given video. Using this option, the conservationists will identify the animal using the developed system which is built with yolov3 (You Look Only Once, version 3) algorithm. Finally, if a pet animal is surrounded by a wild animal, or if both pet and wild animals are present in the video, an alarm indication is given in order to protect domestic animals from the wild animals and also safeguard the agriculture fields
Cognitive Behavioral Therapy using Convolutional Neural Network and Computer Vision

Facial emotion recognition is a crucial aspect of understanding human behavior and has gained significant attention in recent years.In this research paper, we propose a novel approach to recommend music, books, and movies based on the recognized emotions using Convolutional Neural Networks (CNNs).Our proposed approach utilizes a CNN model to accurately detect emotions from facial expressions, and subsequently recommends appropriate music, books, and movies that align with the recognized emotions.We evaluate the proposed approach on a dataset of facial expressions and demonstrate its effectiveness in accurately recognizing emotions and providing suitable recommendations.The results suggest that our proposed approach outperforms existing emotion recognition methods and can provide personalized recommendations based on facial expressions, which could have significant implications in areas such as marketing, entertainment, and mental health.
Digital Security &amp; Social Networks

Social networks are of utmost popularity in current era.People from various age groups are part of them as it attracts the user and totally engage them.As most of the social networks are publicly available for everyone, digital security is being compromised due to careless use of it.There arises number of vulnerabilities of threats which mostly the user is not aware of.These vulnerabilities could be addressed by possible counter measures.This paper is a comprehensive study on social networks, possible attacks on digital data and various security measures that user should adopt to protect his identity and information on social networks.Answers to general question of "What to share?", "how much to share" and "how is being shared?" are being discussed that will give necessary awareness to the non-professional user to be in social network smartly; in secure and protected environment as before.
Joint device architecture algorithm codesign of the photonic neural processing unit

The photonic neural processing unit (PNPU) demonstrates ultrahigh inference speed with low energy consumption, and it has become a promising hardware artificial intelligence (AI) accelerator. However, the nonidealities of the photonic device and the peripheral circuit make the practical application much more complex. Rather than optimizing the photonic device, the architecture, and the algorithm individually, a joint device-architecture-algorithm codesign method is proposed to improve the accuracy, efficiency and robustness of the PNPU. First, a full-flow simulator for the PNPU is developed from the back end simulator to the high-level training framework; Second, the full system architecture and the complete photonic chip design enable the simulator to closely model the real system; Third, the nonidealities of the photonic chip are evaluated for the PNPU design. The average test accuracy exceeds 98%, and the computing power exceeds 100TOPS.
How Scientists Can Safeguard Themselves Online

Scientists are always at risk of digital harassment by bad actors looking to undermine scientific credibility and progress, but there are protective steps they can take.
An improved hybrid structure learning strategy for Bayesian networks based on ensemble learning

In the application of Bayesian networks to solve practical problems, it is likely to encounter the situation that the data set is expensive and difficult to obtain in large quantities and the small data set is easy to cause the inaccuracy of Bayesian network (BN) scoring functions, which affects the BN optimization results. Therefore, how to better learn Bayesian network structures under a small data set is an important problem we need to pay attention to and solve. This paper introduces the idea of parallel ensemble learning and proposes a new hybrid Bayesian network structure learning algorithm. The algorithm adopts the elite-based structure learner using genetic algorithm (ESL-GA) as the base learner. Firstly, the adjacency matrices of the network structures learned by ESL-GA are weighted and averaged. Then, according to the preset threshold, the edges between variables with weak dependence are filtered to obtain a fusion matrix. Finally, the fusion matrix is modified as the adjacency matrix of the integrated Bayesian network so as to obtain the final Bayesian network structure. Comparative experiments on the standard Bayesian network data sets show that the accuracy and reliability of the proposed algorithm are significantly better than other algorithms.
General Extremal Field Method for Time-Optimal Trajectory Planning in Flow Fields

We present an algorithm to compute time-optimal trajectories for light vehicles in unsteady flow fields, with applications to long-range, low-power aircraft as well as underwater vehicles in ocean currents. The proposed approach aims at unifying various works from the literature on extremal fields and extends it by several features. First, we propose an exact scheme to deal with still obstacles. While being directly useful for pure obstacles, it is also of particular interest to ensure the validity of computation at the borders of the problem domain. Second, we demonstrate the method ability to deal with trajectory planning for long-range airborne missions with real weather data. Lastly, the source code, written in Python, is made open to the community to accelerate research in the domain.
Enhancing Signature Verification through Neural Network Ensemble

This study presents a signature authentication mechanism to prevent forgery. In the actual world, handling a large collection of data and detecting genuine signatures with reasonable accuracy is often difficult for any verification system. As a result, artificial intelligence techniques are used that can learn from a large data set during the training phase and reply effectively during the application phase without wasting a lot of storage memory space or processing time. It should also be able to refresh its expertise based on real-world encounters on a regular basis. A Multi-Layered Neural Network Model is one such adaptive machine learning technique that is used in this study. Initially, a massive amount of data is gathered by photographing several authentic and fake signatures. The image quality is increased by applying image processing, which is followed by the feature extraction phase, which extracts specific unique standard statistical features.
Smart Distributed Multi-Ledger Construction Algorithm for Internet of things (IoT)

The Internet of Objects (IoT) could be a new paradigm for the linked, data-exchanging nature of things and their parts through a network supported by nodes. IoT has risen to the forefront of emerging technologies in a very type of applications, including peer-to-peer networks, smart energy grids, home and building automation, vehicle-to-vehicle communication, and wearable computing devices, because of the widespread use of small devices and embedded sensor frameworks. thanks to its tremendous expansion and widespread use, it's posed security issues that may prevent it from getting used during a style of new applications. The growing number of networked gadgets creates several access points for attackers, also as security issues. due to the fragile nature of IoT applications including health, automation, and energy grids, security concerns can't be tolerated. DLT (Distributed Ledger Technology) is one such technology which will help to mitigate IoT security threats. The vulnerability of a central node, which can compromise the whole system, are often reduced by adopting a distributed ledger to eliminate the necessity for one node. Blockchain, a distributed ledger technology, has sparked plenty of interest and generated real-world value. Here we construct a distributed multi ledger construction algorithm for peer-to-peer data transaction which involves key generation for each transaction.
A proposed Vision for the Leadership of Education Institutions on Gifted Caring in Light of Participative Leadership

The study aims to identify the status of gifted caring in education institutions in the Kingdom of Saudi Arabia (KSA).It also attempts to identify the degree of practice by the two heads of participative leadership from the perspective of supervisors in the management of gifted caring.Thus, the study eventually aims to provide a proposed vision for education institutions of gifted caring in light of participative leadership.The study adopted the descriptive survey approach together with the descriptive-analytical method.The study sample involved (102) supervisors.The population comprised all male and female supervisors working for the ministry of education in KSA whose number amounted to (206) supervisors in addition to all ministry circulars related to gifted caring and all relevant studies that tackled education institution leadership in light of the precipitative type.The researchers designed a questionnaire of four dimensions which included (31) items.The study's foremost results revealed that the administration leadership status ranked "medium."As for the practice of the two heads of the gifted, caring leadership, it also ranked "medium" for all domains, which are: management duties, authorization, human relations, and finally, media and communication domains.The researchers also proposed a vision for the leadership of educational institutions for gifted caring in light of participative leadership by designing an integrated model for the issue.The researchers also put down specific steps to be followed in applying the vision.The study concludes with numerous recommendations and suggestions.
Imagination and real quantization

The paper shows that the use of mathematical imaginaries, complex numbers, and even more so quaternions in the Quantum Theory to increase the dimension of solutions of one-dimensional equations is, in principle, incorrect. As a result, Einstein&#39;s condition was violated: SOME equations of Classical Physics can be rewritten in operator form. The use of correct multidimensional models makes it possible, using the example of the Harmonic Oscillator, to obtain the Planck-Einstein Quantization for da Broglie waves.
A Scaling Factor Based Image Processing Strategy for Object Detection

Classified management of domestic garbage is conducive to controlling pollution, protecting the environment, saving resources and achieving sustainable urban development. To automate domestic garbage classification and improve classification rate and processing capacity, this paper innovatively proposes an image processing strategy to detect domestic garbage objects using domestic garbage images as a dataset and YOLOv5 network. The network is then fine-tuned to achieve object detection of domestic garbage. Experimental results show that after using the image processing strategy, mAP@.5:.95 of the first-class (4-class) and second-class (104-class) networks on the basic test set is increased from 15.4% and 10.9% to 28.4% and 18.5%, respectively. This demonstrates the feasibility and effectiveness of the proposed image processing strategy. In addition, the image processing strategies presented in this paper have the potential to be applied in the domain of video recognition, including Sign Language Translation and Lip-reading Recognition.
A Personalized POI Recommendation Algorithm Using BERT-ACNN-GRU

This paper proposes a point-of-interest (POI) sequence recommendation algorithm based on BERT-ACNN-GRU to address the issues faced by the existing POI recommendation model in social network large data, such as the difficulty in extracting deep feature information and the low recommendation performance. Firstly, the semantic relationship between a word and its context in the text is combined using the bidirectional encoder representation from transformers (BERT) model to effectively eliminate the influence of word distance and obtain the contextualized word vector. Secondly, a convolutional neural network (CNN) utilizing a gated recurrent unit (GRU) is employed to capture the feature information of the text. Lastly, the attention method is utilized to assign weight scores to various terms in order to provide more attention to particular words and boost the precision of recommendations. The experiments demonstrate that the precision, recall rate, F1 score and mean average precision (mAP) of the proposed method are 0.097, 0.26, 0.103, and 0.085 on the Gowalla dataset when the recommendation list has a length of 10, respectively. On the Yelp dataset, the precision is 0.093, the recall rate is 0.26, F1 is 0.099, and MAP is 0.089. Hence, the proposed method can effectively enhance the performance of the POI recommendation system.
Analysis of Continuity of Reading Passages in the 5th and 6th Grade English Textbooks: Focusing on Lexical Information

본 연구의 목적은 Coh-Metrix를 활용하여 초등학교 5, 6학년 영어 교과서의 읽기 지문의 어휘 정보를 분석하고, 해당 영역에 있어 각 교과서가 수평, 수직성 연계성을 갖추고 있는지 분석하는 것이다. 이를 위해 각 학년 영어 교과서 5종의 읽기 지문을 코퍼스로 구축한 후 웹 기반 텍스트 분석 소프트웨어인 Coh-Metrix를 통해 각 지문의 어휘 정보(대명사 발생률, 단어 빈도 수, 습득 나이, 친숙도 등)를 분석한 후, 학년 간, 출판사 간 연계성을 살펴보았다. 출판사 간 수평적 연계성을 살펴본 결과 6학년 교과서에서는 어휘 빈도 부분에서 통계적으로 유의한 수준의 차이가 발견되었다. 학년 간 수직적 연계성을 살펴본 결과 모든 출판사의 6학년 교과서가 5학년에 비해 대명사 출현율이 낮았다. 교과서 편찬 시 수평적, 수직적 연계성 확보를 위해서는 본 연구에서 살펴본 것과 같은 다양한 측정치를 고려해야 할 것이다. 그리고 이러한 측정치들은 교과서 선정에도 활용될 필요가 있다. The purpose of this study is to analyze the word information of reading passages in English textbooks for 5th and 6th graders in elementary schools using Coh-Metrix, and to examine whether each textbook possesses horizontal and vertical continuity in this area. To achieve this, reading passages from five English textbooks for 5th and 6th grade were compiled into a corpus, and the lexical information of each passage (pronoun occurrence rate, word frequency, acquisition age, familiarity, etc.) was analyzed using the web-based text analysis software, Coh-Metrix. Subsequently, the continuity between grades and publishers was investigated. In examining the horizontal continuity among publishers, there were statistically significant differences among 6th grade textbooks in terms of word frequency among word information. Upon examining the vertical continuity between grades, it was found that the pronoun occurrence rate was lower in 6th-grade textbooks compared to 5th-grade textbooks for all publishers. For both of horizontal and vertical continuity, various indices should be taken into consideration when authoring English textbooks. And the features of students and different kinds of indices need to be considered during process of choosing English textbooks, too.
Human Resource Management Practices In Himachal Road Transport Corporation

Human resources is the “real wealth creators of the organization.” Human resource management comprises assessing andmanaging the total knowledge, skills, creative abilities, ethics, and talents of the workforce. Fruitful planning, excellent human resource management, business-friendly policies &amp; procedures, and proactive government can bring sustainable success to an organization. Human resource management (HRM) practices include human resource planning, recruitment and selection, training and development, compensation and benefits, performance appraisal system, industrial relations, career planning &amp;development, organizational culture, and commitment &amp;job satisfaction. The present study proposes to evaluate HRMpracticesfollowed in the public sector transport undertaking of Himachal Pradesh, viz., Himachal Road Transport Corporation (HRTC). The study mainly focuses on examining the perceptions of HRTC employees and their satisfaction level towards HRM practices followed in HRTC. Sample consists of 120 respondents of Shimla district of Himachal Pradesh and the judgmental sampling method has been followed. The conclusion drawn from the study is that HRM practices have a major impact on satisfaction level of employees of HRTC.
Implementasi Kebijakan “Mappadeceng” Dalam Bidang Pendidikan di Kabupaten Soppeng

This study aims to analyze the implementation of the "Mappadeceng" policy in the education sector in Soppeng Regency by using descriptive qualitative methods. Data was collected by means of in-depth interviews, observation, and documentation of 6 informants. Data analysis was performed interactively consisting of data reduction, data presentation and drawing conclusions. The results show that the implementation of the "mappadeceng" policy in the education sector has not been going well, this is because the size and policy objectives are not yet clear, the availability of human resources and budget is adequate, the implementing agency has a role based on the stipulated decree, the disposiion of implementing agents it is good enough, the lack of communication and coordination carried out, both with the community and related organizations and the external environment consisting of economic, social and political conditions is quite conducive and supports the implementation of the “mappadeceng” policy. Therefore, it is recommended that the education and culture office of Soppeng Regency to conduct evaluation in increasing the success of implementing the “Mappadeceng” policy.
Optimised fuzzy C-means clustering based cluster indexing towards incremental learning integrated text categorisation

Text classification is the most significant task in the data retrieval process through classifying text into various groups depending on the document's content. The quick progression of electronic documents may produce various issues, such as unstructured data, which requires more effort and time for searching appropriate documents. Text categorisation has high importance in information retrieval and processing, wherein unstructured documents are arranged into a predefined group. In addition, incredible growth in online documents obtains ability with the development of the internet needs a highly precise and effective retrieval approach. Thus, in this paper, Dingo Monarch butterfly optimisation (DMBO) approach and Tversky index-based indexing are developed for incremental learning-enabled text categorisation. Moreover, text categorisation is done based on incremental learning along with a Bayesian classifier. This text classification approach achieved better performance with a precision of 0.9136, recall of 0.9173, F-measure of 0.9051, and accuracy of 0.8461.
Explainable Convolutional Neural Networks for Assessing Head and Neck Cancer Histopathology

Background Deep Learning algorithms show remarkable performance in the analysis of histopathological slides. We applied Convolutional Neural Networks and Explainable AI techniques to classify head and neck cancer.
Securing LSB steganography using bite substitution and image blocking

Protecting secret messages is a vital issue, in this paper’s research, a simplified, highly secure method of message steganography will be introduced. The proposed method will use a complicated PK, which contains information to select a secret block from the color image to be used as a covering block; also it will contain the values of the chaotic logistic map model to run this model to generate the indices key needed to substitute the message binary matrix. The PK will provide a huge keyspace capable to resist hacking attacks, the extracted message will be very sensitive to any minor changes in the PK, and any changes in this key during the extraction phase will be considered a hacking attempt by producing a damaged extracted message. It will be shown that the proposed method will be always efficient when changing the message length and changing the covering images. The proposed method will be implemented using various messages and various covering images, the obtained results will be analyzed using various types of data analysis methods to prove the improvements provided by the proposed method (quality, security, and efficiency).
STUDENTS’ ABILITY AND DIFFICULTIES IN CONSTRUCTING CONDITIONAL SENTENCE TYPE II AT SMAN 10 KERINCI JAMBI

Conditional sentences Type II are an important part of communication, especially in expressing hypothetical situations. This can help students understand how to communicate more effectively, and is an important skill in language proficiency and can help identify areas that need improvement, leading to the development of better language skills. This study identifies students' difficulties in understanding and reproducing the type II conditional sentence by using grammar test and interviews. This study aims to determine students' abilities in constructing type 2 conditional sentences and the problems faced by students. The research design is descriptive. The population is all grade II students of SMAN 10 Kerinci Jambi Province and the sample is students of class XI MIPA I. The research instruments used were grammar tests and interviews. The results showed that the average ability of students to construct conditional sentences was low, with an average score of 62 (categorized C). The difficulties faced by students were confusion in the use of verb-2 and to be for the past tense, students' ignorance of irregular verbs, confusion in determining causal sentence parts in conditional sentences. Finally, sentence conditioning type 2 is difficult, but it can be learned if students practice more in constructing sentences.
A BRIEF DISCUSSION ON MOTIVATION OF CHINESE LANGUAGE LEARNING FOR STUDENTS MAJORING IN TRANSLATION STUDIES AT CONFUCIUS INSTITUTE OF SOUTH UKRAINIAN NATIONAL PEDAGOGICAL UNIVERSITY NAMED AFTER K. D. USHYNSKY

As the object and subject of teaching activities, the learners’ subjective motivation in learning has a crucial influence on the final learning effect. Therefore, the exploration of learners’ motivation and its influencing factors is of great practical significance to the development of teaching activities and the final achievement of teaching results. In this paper, we take the second-year translation class 2 of Confucius Institute as the object of teaching observation and motivation of Chinese language learning. By finding and analysing the relationship between students’ problems in Chinese learning and their motivation in the new Chinese learning cycle, we try to select a targeted method from the perspective of maintaining and further stimulating students’ Chinese learning motivation and make adjustments according to the actual application effect of the method. The aim is to find out a set of strategies for motivating students’ Chinese language learning in three different stages: before, during and after classes, so that students can gradually move from being able to learn to learning well and enjoying learning.