Reviewerly/exHarmony huggingface dataset:
Design and Application of γ-Ray Energy Spectrum Survey System Based on UAV

Abstract Airborne γ-ray energy spectrum measurement is an effective method for radioactive mineral investigation and radiation environment monitoring. In recent years, with the gradual maturity of Unmanaged air vehicle (hereinafter referred to as UAV) technology, UAV airborne geophysical survey technology is becoming a hot research field. But the UAV has less load and space and is difficult to carry traditional aeronautical geophysical equipment. The miniaturization, intelligentization and automation of airborne geophysical survey instruments are the key technologies of airborne geophysical survey for UAVs. The γ-ray energy spectrum measurement system is designed suitable for UAV platform with load in the range of 30–180 kg, at the same time, the data acquisition control and remote monitoring software is developed. The calibration of the system is completed on the Shijiazhuang airborne standard model and Huangbizhuang dynamic belt, and the calibration parameters are obtained, the resolution of the crystal is better than 8.5%, and the peak drift is better than ±1, the sampling frequency of energy spectrum data is 1 Hz. Based on SY-120H and CH-3 UAV platforms, test flights were carried out in Karamay, Xinjiang and Erlianhot, Inner Mongolia, respectively, to find out the distribution of radioactive field in the test area and quickly delineate the specific location and range of the radioactive anomaly. The test results show that: UAV aviation γ The results of K, U and Th content measured by energy spectrum are basically consistent with those measured on the ground, which can provide a fast and efficient exploration technology and equipment for radioactive mineral investigation and radiation environment monitoring.
Rancang Bangun Bot Pengumuman Di Jurusan Teknik Informatika Universitas Palangka Raya Di Aplikasi Telegram

Announcement is a form of delivering information addressed to the general public. Announcement is a letter addressed to several people, agencies, or other parties whose names are too many to mention one by one. In the announcement in the Department of Informatics, University of Palangka Raya, it still uses a manual system. In a way that is still manual, it takes a lot of time in the process. Like what students have to do, they have to come check the latest announcements, and see new announcements through Whatsapp groups and Telegram groups. Therefore, with this problem, in the process of solving it, an Announcement Bot was made at the Department of Informatics, University of Palangka Raya on the Telegram Application which can complete the completion of finding the latest information by students. The stages of modeling the Announcement Bot in the Department of Informatics, University of Palangka Raya in the Telegram Application using the waterfall research methodology. Where, this methodology has several stages, namely, requirements definition, system and software design, implementation and unit testing, integration and system testing and operation. This Announcement Bot uses the Long Polling method. Where, this method the server will periodically check to the bot whether there is an incoming message. If there is an incoming message, the server will execute based on the request message sent by the user. Testing this system uses a User Acceptance Testing (UAT) testing system. The test results will be used as evidence that the software has been received and has met the requested requirements. This research produces an Announcement Bot at the Department of Informatics, University of Palangka Raya on the Telegram Application that can help find information in the Department for Students.
Security printing methods from a digital printing perspective

The paper discussed the secured prints by digital printing method, the paper has reviewed some new programs features provided in the field.paper problem stated to study the lack of using the new anti-counterfeiting design features in addition to future using of digital printing method as a new method in preventing prints or goods anti-counterfeiting. paper methodology followed the analytical experimental approach and theoretical one' to solve paper problem.the objectives were to find the influence of those new innovations in doing the experimental, continue with the tests and its analysis to identify a fair and clear solution to the paper problem. in order to do that, the paper has to study the first part of paper research which focuses on one of security new programs, courtesy of agfa ... etc, and types of inks which is considered one of the most important elements in securing materials especially the UV inks, IR inks and taggant inks.The designs include covert elements used the invisible UV inks.semi-covert design elements include microtext, authenticate secure barcode, line in line special screen and other elements used visible Electroinks.The overt elements include Guilloche, latent image, random pattern and other elements solution have also been used visible Electro-inks.Whereas, The second part focused on the analytical of the digital printing experimental which has been printed for the packages field, discussing the printed secured prints designs with a wide range of several substrates, the tests analysis were all about ink resistant to several agents, tests analysis has been implemented according to ISO Standards, the evaluation of the tests and the paper results showed that the prepared secured documents printed with UV inks on the selected digital printing corporation innovation machine (courtesy of Hewlett-Packard) have a very good performance in durability even with the hard tests which carried out on the printed samples.Recommendation included that the security digital printing and the new digital program features, both of them have an excellent opportunity in securing a very large sectors of low valued documents in addition to some of the valued documents except the forensic features which are not available by the digital printing yet.
Self-Reinforcing Feedback Domain Adaptation Channel

Unsupervised domain adaptation methods utilize feature re-presentations of instances in the source and target domains to eliminate domain shifts. It is worth noting that the instance features are closely related to the entire distribution of the domain, and the current information after adaptation in the execution of the domain adaptation task is closely related to the original features. Common methods are based on only one of these pieces of information and do not make sufficient use of them. We develop the Self-Reinforcing Feedback Domain Adaptation Channel (SRFC). Pioneeringly, on the feature representation of the network, SRFC fuses global and instance information simultaneously, and utilizes the past history and current information in domain adaptation, so that the information can be effectively enhanced to better complete the domain adaptation. Through the designed self-reinforcing feedback mechanism, SRFC skillfully integrates multi-level information in a robust way in the process of domain adaptation, and actively enhances the availability and comprehensive value of features in domain adaptation with manageable continuous feedback. Experiments on benchmark datasets verify the advantages of SRFC fusion information for instance feature enhancement and domain adaptation. The modular Self-Reinforcing Feedback Domain Adaptation Channel has scalability and R &D potential, and we hope that it can be extended to more domain adaptation networks using enhanced instance representations to better accomplish different tasks.
FFT-Based Dual-Mode Blind Watermarking for Hiding Binary Logos and Color Images in Audio

Watermarking technology is a feasible approach to prevent copyright infringement. The practicality of the watermark itself affects the acceptance and penetration of relevant applications. In this study, a dual-mode FFT-based audio watermarking framework is devised to achieve both binary and numeric data hiding concurrently using respective modulations. We demonstrate: (1) the adaptive magnitude modulation (AMM) applied to large-magnitude FFT coefficients is competent to deliver efficient and effective binary watermarking at a rate of 678.46 bits per second; (2) the phase modulation (PM) in charge of the embedding of pixel values directly into medium-size FFT coefficients can operate at a rate of 508.85 numbers per second. With a tactical selection of embedding positions and strengths, both the FFT-based AMM and PM exhibit exceptional robustness against commonly encountered attacks. According to the perceptual evaluation of audio quality (PEAQ) metric, the resulting audio quality remains indistinguishable from the original audio, even at the aforementioned high payload capacity.
Research and application of keyword matching engine for online behavior

This paper introduces a method of internet behavior detection. With the rapid development of society, the pace of people's life is faster and faster, and the competition is more and more fierce in all aspects. For example, college students often encounter mental imbalance in employment, study, life, love and self-consciousness. Now, the network, data and other concepts have been very closely embedded in people's life, study and work. A college student using a mobile device can generate hundreds of megabytes data during one day. By using Intel's open source Hyperscan engine, we can analyze the data of internet behavior by using the keyword matching technology of internet traffic, so as to quickly and effectively identify and warn psychological crisis.
Handbook on HR Process ResearchSandars, K., Yang, H., &amp; Charmi, P. (Eds.). (2021, p. 224) Edward Elgar Publishing, $46.52 paperback

Personnel PsychologyVolume 76, Issue 2 p. 725-727 BOOK REVIEWS Handbook on HR Process Research , Sandars, K. , Yang, H. , & Charmi, P. (Eds.). (2021, p. 224) Edward Elgar Publishing, $46.52 paperback Li-Qun Wei, Corresponding Author Li-Qun Wei [email protected] orcid.org/0000-0002-0739-8379 Hong Kong Baptist University, Hong Kong, P R China Correspondence Li-Qun Wei, Hong Kong Baptist University, Renfrew Road, Kowloon Tong, KL, Hong Kong, P R China. Email: [email protected]Search for more papers by this author Li-Qun Wei, Corresponding Author Li-Qun Wei [email protected] orcid.org/0000-0002-0739-8379 Hong Kong Baptist University, Hong Kong, P R China Correspondence Li-Qun Wei, Hong Kong Baptist University, Renfrew Road, Kowloon Tong, KL, Hong Kong, P R China. Email: [email protected]Search for more papers by this author First published: 13 April 2023 https://doi.org/10.1111/peps.12589Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume76, Issue2Summer 2023Pages 725-727 RelatedInformation
Chemical elements, minerals and rocks in a single three-dimensional model

The purpose of the study is refining of the proposed earlier unified three-dimensional model for the positioning of chemical elements, minerals, and rocks in the earth's crust, as well as working out its description terminology and model system properties specification. The research methods included the logical and mathematical analysis of the model based on atomic weights of chemical elements, their valences and electronegativity, as well as on the atomic weights of chemical elements present in the composition of minerals and calculation of multidimensional proportionality indexes for each of given characteristics. The methods were used for further development of the previously introduced assumption that within the three-dimensional model minerals are arranged in the ascending order of the index of multidimensional proportionality of atomic weights of chemical elements in their composition, similarly to the way the chemical elements are positioned in the model according to the increase of their atomic weights. Both rocks and minerals are arranged in the ascending order of this index, whereas rocks correspond to a certain mineral in terms of the multidimensional proportionality index. As a result of the study, a general analysis of the system properties of vertical spatial groups of chemical elements of the model was carried out with groups being divided into two types depending on the presence of a gas or a solid chemical element in the first position. The variation patterns of chemical element valencies and electronegativity were studied in these spatial groups. Also, the analyses of multidimensional data were performed for the elements of these groups. The values of the multidimensional index were calculated for the first 760 minerals using the Agemarker open source program. The analysis of multivariate data confirmed the division of groups into two types. Having analyzed their mutual arrangement, the authors suggested the existence of gas channels of chemical element migration and determined possible migration paths of chemical elements in the three-dimensional model under the formation of some minerals. The relative position of some fields was considered in order to confirm the spatial characteristics of the three-dimensional model. The authors have introduced a new paradigm of geological research positioning minerals and rocks in a three-dimensional model and determining possible migration paths of chemical elements when minerals and rocks are formed.
A study on the application of multilevel secure hash function in blockchain

With the rapid development and upgrade of the social network and IT, information security has become increasingly significant. Hash function has become a research key-point in the field of information security. To meeting the requirements of various circumstances, this dissertation puts forward Multilevel Security Hash Function (MSHF) and conduct a comparison experiment between MSHF and traditional methods to generate Merkle trees. The experimental results show that the Merkle tree generated based on MSHF has smaller depth, shorter path length, and shorter time used to find the wrong data.
Application of BeiDou-3 navigation satellite system in hydrometeorological observation system

According to the transmission capacity currently provided by the Beidou-3 system, a data transmission system based on the Beidou-3 Navigation Satellite System (BDS-3) was designed to address the problems of the original data transmission of the hydro-meteorological observation system. The data transmission protocol was defined, and the data transmission message was composed of multi-minute observation data superimposed and encrypted by DES. The active self-reporting data transmission scheme and the interactive query data transmission scheme were elaborated. Data security is ensured by both data encryption and BDS-3 satellite communication; the data transmission message consists of multi-minute observation data overlay to ensure the arrival rate of real-time observation data; and the data integrity is ensured by the interactive query method.
Pedestrian action recognition in infrared image based on improved GoogLeNet

Aiming at the problem of pedestrian behavior recognition in infrared images, a method based on Improved GoogLeNet is proposed. Firstly, by analyzing the application scenarios and the characteristics of common network models, GoogLeNet with better comprehensive performance is selected as the backbone network. Inspired by NIN, a kind of 1*1 convolution kernel structure is introduced to reduce the number of channels and significantly reduce the number of parameters. Then channel padding and resize to adapt to the network requirements for the training set and test set of the infrared image human behavior data set. Next, the fully connected layer and the classification output layer of the network are modified according to the number of behavior types contained in the data set. The convolution kernel and inception parameter in the pre-training network are introduced to accelerate the network training and improve the generalization ability of the network. Finally, the quantitative index is used to analyze the experimental results and judge the recognition performance of the network. Experimental results shows that the Mean Average Precision, Average Recall and F1 score obtained by the proposed algorithm are better than the traditional methods.
Optimal Geodesic Curvature Constrained Dubins’ Paths on a Sphere

In this article, we consider the motion planning of a rigid object on the unit sphere with a unit speed. The motion of the object is constrained by the maximum absolute value, $$U_{max}$$ , of geodesic curvature of its path; this constrains the object to change the heading at the fastest rate only when traveling on a tight smaller circular arc of radius $$r <1$$ , where r depends on the bound, $$U_{max}$$ . We show in this article that if $$0<r\le \frac{1}{2}$$ , the shortest path between any two configurations of the rigid body on the sphere consists of a concatenation of at most three circular arcs. Specifically, if C is the smaller circular arc and G is the great circular arc, then the optimal path can only be CCC, CGC, CC, CG, GC, C or G. If $$r>\frac{1}{2}$$ , while paths of the above type may cease to exist depending on the boundary conditions and the value of r, optimal paths may be concatenations of more than three circular arcs.
A Multi-channel Next POI Recommendation Framework with Multi-granularity Check-in Signals

Current study on next point-of-interest (POI) recommendation mainly explores user sequential transitions with the fine-grained individual-user POI check-in trajectories only, which suffers from the severe check-in data sparsity issue. In fact, coarse-grained signals (i.e., region- and global-level check-ins) in such sparse check-ins would also benefit to augment user preference learning. Specifically, our data analysis unveils that user movement exhibits noticeable patterns w.r.t. the regions of visited POIs. Meanwhile, the global all-user check-ins can help reflect sequential regularities shared by the crowd. We are, therefore, inspired to propose the MCMG: a Multi-Channel next POI recommendation framework with Multi-Granularity signals categorized from two orthogonal perspectives, i.e., fine-coarse grained check-ins at either POI/region level or local/global level. The MCMG is equipped with three modules, namely, global user behavior encoder, local multi-channel (i.e., region, category, and POI channels) encoder, and region-aware weighting strategy. Such design enables MCMG to be capable of capturing both fine- and coarse-grained sequential regularities as well as exploring the dynamic impact of multi-channel by differentiating the check-in patterns w.r.t. visited regions. Extensive experiments on four real-world datasets show that our MCMG significantly outperforms state-of-the-art next POI recommendation approaches.
Cloud Task Scheduling Using Modified Penguins Search Optimization Algorithm

The cloud computing has emerged as a novel distributed computing system in past few years. It provides computation and resources over the Internet via dynamic provisioning of services. There are quite a few challenges and issues connected with implementation of cloud computing. This paper considers one of its major problems, i.e. task scheduling. The function of an efficient task scheduling algorithm is that it concentrates not only on attaining the requirements of the user but also in enhancing the efficiency of the cloud computing system. Cloud task scheduling is an NP-hard optimization problem, and many meta-heuristic algorithms have been proposed to solve it. This paper proposes a modified Penguins Search Optimization Algorithm (MPeSOA) for efficient cloud task scheduling. The main contribution of our work is to schedule all tasks to available virtual machines so that the makespan is minimized, resource utilization is increased and the degree of imbalance is reduced. The proposed scheduling algorithm was simulated using the CloudSim 4.0 simulator. Experimental results showed that the proposed MPeSOA outperformed three existing meta-heuristics, namely Penguins Search Optimization Algorithm (PeSOA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO).
Pengembangan Modul Layanan Informasi Karir Berbasis Ensiklopedia Bergambar Untuk Pemahaman Karir

Career information service guidance is important for students because students are still confused about choosing a career. The purpose of this study was to produce a career information module media for class XI of the Accounting Department that students can use as a guide for career selection, and can be used as a guide for guidance and counseling teachers. This study uses 10 stages of the Borg &amp; Gall (2021) model which are adjusted into 6 stages. Collecting data using interviews, questionnaires, and documentation. The product validation formula is used to obtain questionnaire results from media experts, material experts, and practitioner experts. The subjects of this study were students of class XI Accounting who were selected by random sampling. The number of research subjects was 10 respondents (small group test) and 5 validators (validation test). The results of the module practicality response questionnaire obtained a score of 92.6%. While media expert validation obtained 90.83% (very feasible), material expert validation obtained 92.5% (very feasible), practitioner expert validation results obtained 94% (very feasible) it can be concluded that the research results regarding the product being developed are acceptable and worth using. Keywords: Module;Career Information; Students
Feature Selection based Breast Cancer Prediction

Breast cancer is one of the main causes of mortality for women around the world.Such mortality rate could be reduced if it is possible to diagnose breast cancer at the primary stage.It is hard to determine the causes of this disease that may lead to the development of breast cancer.But it is still important in predicting the probability of cancer.We can assess the likelihood of occurrence of breast cancer using machine learning algorithms and routine diagnosis data.Although a variety of patient information attributes are stored in cancer datasets not all of the attributes are important in predicting cancer.In such situations, feature selection approaches can be applied to keep the pertinent feature set.In this research, a comprehensive analysis of Machine Learning (ML) classification algorithms with and without feature selection on Wisconsin Breast Cancer Original (WBCO), Wisconsin Diagnosis Breast Cancer (WDBC), and Wisconsin Prognosis Breast Cancer (WPBC) datasets is performed for breast cancer prediction.We employed wrapper-based feature selection and three different classifiers Logistic Regression (LR), Linear Support Vector Machine (LSVM), and Quadratic Support Vector Machine (QSVM) for breast cancer prediction.Based on experimental results, it is shown that the LR classifier with feature selection performs significantly better with an accuracy of 97.1% and 83.5% on WBCO and WPBC datasets respectively.On WDBC datasets, the result reveals that the QSVM classifier without feature selection achieved an accuracy of 97.9% and these results outperform the existing methods.