Reviewerly/exHarmony huggingface dataset:
IMPLEMENTATION OF MULTI-CLASS GRADIENT BOOSTING TO CLASSIFY ANIMAL SPECIES IN ZOOS

Animals are one of the living things that have various types. Grouping types of animals based on similarities and differences in characteristics owned is one of the important activities carried out To make it easier to compare, recognize, study certain types of animals and be able to find out kinship relationships between animals, So if a new type of animal is found that does not yet have a name, it will be easier for us to give a name to the animal based on the type and based on the group. In research on the classification of animal species in zoos that have multi-class, the best classification is obtained by applying gradient boosting parameters with n_estimators of 50, max_depth 3, sub-sample of 1.0, learning rate of 0.1, and using criterion friedman Mse. And by implementing Split validation or division between training data by 80% for training data and 20% for testing data. The results stated that the proposed model was better than some other models that had also been tested with an accuracy value of 93.75%, recal of 94%, precision of 96% and MSE to measure the average magnitude of error in a series of classifications of 12.5%, the smaller the MSE value, the better it would be in classifying.
Preface

Preface&#x0D; The 19th International Symposium on Scientific Computing, Computer Arithmetic and Verified Numerical Computation (SCAN) was originally planned to be organized by the Institute of Informatics of the University of Szeged (SZTE) in Szeged, Hungary, in the year 2020. Due to the pandemic situation, the Scientific Committee of SCAN decided to postpone the meeting to September 13–15, 2021 and to have it in a fully online format.The members of the Scientific Committee were the following representatives of the topics of the conference: G. Alefeld (Karlsruhe, Germany), A. Bauer (Ljubljana, Slovenia), J. B. van den Berg (Amsterdam, the Netherlands), G.F. Corliss (Milwaukee, USA), T. Csendes (Szeged, Hungary), R.B. Kearfott (Lafayette, USA), V. Kreinovich (El Paso, USA), J.-P. Lessard (Montreal, Canada), W. Luther (Duisburg, Germany), S. Markov (Sofia, Bulgaria), G. Mayer (Rostock, Germany), J.-M. Muller (Lyon, France), M. Nakao (Tokyo, Japan), T. Ogita (Tokyo, Japan), S. Oishi (Tokyo, Japan), K. Ozaki (Tokyo, Japan), M. Plum (Karlsruhe, Germany), A. Rauh (Brest, France), N. Revol (Lyon, France), J. Rohn (Prague, Czech Republic), S. Rump (Hamburg, Germany/Tokyo, Japan), S. Shary (Novosibirsk, Russia), W. Tucker (Uppsala, Sweden), W. Walter (Dresden, Germany), J. Wolff von Gudenberg (Würzburg, Germany), and N. Yamamoto (Tokyo, Japan). The members of the Organizing Committee were: Balázs Bánhelyi, Tibor Csendes, Boglárka G.-Tóth, Viktor Homolya, Tamás Vinkó, and Dániel Zombori. During SCAN, more than 50 participants were present and 48 talks in several fields of reliable computation and its applications were given, and organized in 18 thematic sessions. The plenary speakers were Fabienne Jézéquel (Sorbonne University, France), Marko Lange (Hamburg University of Technology, Germany), J.D. Mireles James (Florida Atlantic University, USA), together with the Moore Prize winners Marko Lange and Siegfried Rump (Waseda University, Japan).The open-access scientific journal Acta Cybernetica offered to publish paper versions of selected presentations after a careful peer review process. Altogether, 7 papers were accepted for publication in the present special issue of Acta Cybernetica. The full program of the conference, the collection of all abstracts, and further information can be found at https://www.inf.u-szeged.hu/scan2020/.&#x0D; Andreas Rauh, Balázs Bánhelyi Guest Editors
Design Development of Detection System and Ro-Ro Ship Notification based on Fuzzy Inference System

Ship stability is very important for the safety of ship motion. There are many factors that affect the stability of a ship. One of the causes of accidents on ships is the problem of ship stability, including the ship cannot be controlled, and loses balance due to improper placement of cargo loads. This study combines gyroscopes, accelerometers, compasses, and GPS sensors, so that more accurate ship tilt information is obtained through an Android smartphone application. This study uses the fuzzy inference system (FIS) method with a trapezoidal membership function where there are 2 inputs and 1 output. Ship tilt input uses 3 linguistic variables very tilted, tilted, and stable. The slope duration input uses 5 very fast, fast, fairly fast, slow, and very slow linguistic variables. Ship status output is divided into 3 linguistic variables safe, alert, and dangerous. Testing and implementation with an input slope of 4.8 and a slope duration of 10 seconds using the Sugeno fuzzy method, the ship's crips value of 0.65 with an alert status was obtained. Calculation of the accuracy of the gyroscope sensor error using the MAPE method, the result is an error percentage of 6.55% (very good). The system accuracy error of 39 trials (36 correct and 3 incorrect) is 92.30% (very good). This research is expected to make it easier for the captain to monitor the stability of the ship and can provide notification of the status of the ship to the captain of the ship if there is a condition of the ship that needs to be watched out for. In addition, the notification will also be received by Port officers on land.
HEaaN.MLIR: An Optimizing Compiler for Fast Ring-Based Homomorphic Encryption

Homomorphic encryption (HE) is an encryption scheme that provides arithmetic operations on the encrypted data without doing decryption. For Ring-based HE, an encryption scheme that uses arithmetic operations on a polynomial ring as building blocks, performance improvement of unit HE operations has been achieved by two kinds of efforts. The first one is through accelerating the building blocks, polynomial operations. However, it does not facilitate optimizations across polynomial operations such as fusing two polynomial operations. The second one is implementing highly optimized HE operations in an amalgamated manner. The written codes have superior performance, but they are hard to maintain. To resolve these challenges, we propose HEaaN.MLIR, a compiler that performs optimizations across polynomial operations. Also, we propose Poly and ModArith, compiler intermediate representations (IRs) for integer polynomial arithmetic and modulus arithmetic on integer arrays. HEaaN.MLIR has compiler optimizations that are motivated by manual optimizations that HE developers do. These include optimizing modular arithmetic operations, fusing loops, and vectorizing integer arithmetic instructions. HEaaN.MLIR can parse a program consisting of the Poly and ModArith instructions and generate a high-performance, multithreaded machine code for a CPU. Our experiment shows that the compiled operations outperform heavily optimized open-source and commercial HE libraries by up to 3.06x in a single thread and 4.55x in multiple threads.
Obtaining Information Leakage Bounds via Approximate Model Counting

Information leaks are a significant problem in modern software systems. In recent years, information theoretic concepts, such as Shannon entropy, have been applied to quantifying information leaks in programs. One recent approach is to use symbolic execution together with model counting constraints solvers in order to quantify information leakage. There are at least two reasons for unsoundness in quantifying information leakage using this approach: 1) Symbolic execution may not be able to explore all execution paths, 2) Model counting constraints solvers may not be able to provide an exact count. We present a sound symbolic quantitative information flow analysis that bounds the information leakage both for the cases where the program behavior is not fully explored and the model counting constraint solver is unable to provide a precise model count but provides an upper and a lower bound. We implemented our approach as an extension to KLEE for computing sound bounds for information leakage in C programs.
Towards Improving 5G Quality of Experience: Fuzzy as a Mathematical Model to Migrate Virtual Machine Server in The Defined Time Frame

The industry and government have recently acknowledged and used virtual machines (VM) to promote their businesses. During the process of VM, some problems might occur. The issues, such as a heavy load of memory, a large load of CPU, a massive load of a disk, a high load of network and time-defined migration, might interrupt the business processes. This paper identifies the migration process among hosts for VM to overcome the problem within the defined time frame of migration. The introduction of VMs migration in a timely manner is to detect a problem earlier. There are workload parameters, such as network, CPU, disk and memory as our parameters. To overcome the issue, we have to follow the Model named Fuzzy rule. The rule follows the basic of tree model for decision-making. The application of the fuzzy Model for the study is to determine VMs allocation from busy VMs to vacant VMs for balancing purposes. The result of the study showed that the use of the fuzzy Model to forecast VMs migration based on the defined rule had 2 positive impacts. The positive impacts are (1) Time frame live migration of VMs can reduce workload by 80 %. This aims to reduce failures in performing a live migration of VMs to increase data center performance. (2) In testing, the fuzzy Model can provide results with an accuracy of 90 %, so this model can perform a live migration of VMs precisely in determining the execution time. Next, the workload could be balanced among VMs. This research could be used further to improve 5G Quality of Experience (QoE) shortly.
Socio-economic Development of Educational Activities in the Context of the Modernization of Lifelong Education

Researched the issue of development educational activities in the context of the modernization of continuing education, in the 1930s. The theoretical substantiation of the development pedagogical process at the present stage. The purpose of the study is to analyze the development of lifelong education at the present stage of socio-economic changes. The process of changing the qualitative indicators of innovative technologies for the organization of the educational process is considered. An example of continuous education in the educational environment of the university through the creation of a technopark as an innovative platform for educational activities is given. The main feature of the development of vocational education is highlighted, which is that it is directly related to the economic development of our country. In this regard, the question arises of the development of vocational education aimed at improving the quality of all levels of education.
MES-Loss: Mutually equidistant separation metric learning loss function

Deep metric learning has attracted much attention in recent years due to its extensive applications, such as clustering and image retrieval. Thanks to the success of deep learning (DL), many deep metric learning (DML) methods have been proposed. Neural networks (NNs) utilize DML loss functions to learn a mapping function that maps samples into a highly discriminative low-dimensional feature space, facilitating measuring similarities between pairs of samples in such a manifold. Most existing methods usually try to boost the discriminatory power of NN by enhancing intra-class compactness in the high-level feature space. However, they do not explicitly imply constraints to improve inter-class separation. We propose in this paper a new composite DML loss function that, in addition to the intra-class compactness, explicitly implies regulations to enforce the best inter-class separation by mutually equidistantly distributing the centers of the classes. The proposed DML loss function achieved state-of-the-art results for clustering and image-retrieval tasks on two real-world data sets.
Processing Optical Temporal Signal Using Optical-Eletrical Feedback of Silicon Optical Modulator Towards Japanese Vowel Recognition

We propose an integrated photonics architecture using optical-electrical feedback of silicon optical modulator to process optical temporal signal for voice recognition. For four Japanese vowels, the training classification accuracy can be >80% with only 20 parameters. We experimentally clarified the role of optical-electrical feedback.
A Multi-Label Classification Method Based On Textual Data Mining

Researches on Big data has been a heated discussion because the rapid development of Internet brings a large amount of valuable data. In this paper, we proposed an effective method to mining the textual data. To be more specific, a multi-label classification method based on textual data mining was designed. The design of this method is built on deep learning. It first adopts Bret pre-training model to express the text, and then be processed with text mining module. This module employs the self-attention mechanism to get the sentence embedding of the text. Meanwhile, convolution is adopted to gain the local semantic information. Then, keyless attention fusion, an effective method used in multi-modal fusion is utilized to aggerate the above two parts. Besides, the value of textual data for reviews about commodities engrossed our attention, as it can heavily help the buying decision of individuals. Thus, our research of textual data focused on the reviews of commodities. Each data has multiple attributes, and that is the reason we chose Multi-label classification in Natural Language Processing as the direction of our model design. We conducted an in-depth discussion of the combination of multi-label classification and the features of those textual data. Finally, we compare our model with other baseline models with evaluation metrics of precision@K, recall@K, F1_score@K and Ndcg@k. Our model gains the best performance in those metrics. With the help of our model, customers can gain precise and objective information about the goods and their purchase decision can be more sensible.
Gesture recognition based on human - computer interaction

The man-machine interaction technology based on gesture recognition has some problems, such as slow speed and low precision of static gesture recognition, and poor expansibility of gesture action. Yolov4-Tiny algorithm based on attention mechanism was proposed, and action semantics was designed by combining basic gestures with gesture state change, and the application function was called according to action semantics, which realize efficient human-computer interaction. By comparing the methods involved in each process, it can be seen that deep learning has strong fault tolerance, robust-ness, high parallelism, anti-interference, etc., which has achieved great achievements above the traditional learning algorithm in the field of gesture identification.
Implementation and optimization of ORB-SLAM2 algorithm based on ROS on mobile robots (Erratum)

Publisher's Note: This paper, originally published on 25 May 2023, was replaced with a corrected/revised version on 6 June 2023. If you downloaded the original PDF but are unable to access the revision, please contact SPIE Digital Library Customer Service for assistance.
End-Users' Perception Of Cybercrimes Towards E-Banking Adoption And Retention

The advancement of information and communication technology (ICT)has resulted in dynamic changes in the operations of various business sectors all over the world, while causing inconvenience to users and making stakeholders' lives easier. However, the advancement in the same has brought major concerns for businesses, such as cybercrime. This study focuses on identifying the impact of cybercrime on the adoption of e-banking services in Pakistan. The study aims to understand the impact of hacking, identity theft and phishing on the adoption of E-banking in Pakistan. The data was collected from 384 banking customers through structured questions containing the demographic questions as well as questions related to the constructs used in the study. The findings of the study suggest that hacking, phishing and identity theft negatively affect the adoption of E-banking in Pakistan. The results of the study are discussed in line with the existing literature. The study limitations and future research areas are also discussed.
CRIMESIGHT:A CRIME TYPE PREDICTION AND COMPLAINT MANAGEMENT SYSTEM

CrimeSight is an innovative web-based application that combines crime type prediction, complaint management, and criminal record management functionalities.The system allows the public to file complaints by providing suspect age and crime location details.It provides users with the ability to view their past complaints and check for responses from law enforcement officials.Administrators have control over adding police stations, locations, and crime types that can be predicted.The app enables police to verify suspects and maintain a comprehensive criminal record database.Using machine learning techniques, the app predicts crime type that may happen on the given location and similar crimes based on suspect age and location, providing valuable insights for investigations on what kind of crimes are most likely to happen on the location specified.CrimeSight aims to improve crime reporting, enhance transparency, and assist law enforcement agencies in allocating resources effectively.
Reinforcement learning transformer for image captioning generation model

Image captioning generation is a combination of the visual domain and natural language processing. The transformer framework has become the mainstream approach. This paper combines reinforcement learning and transformer methods to reward dynamics backpropagation and normalization in the testing phase. Its characteristic is that when the steps of reinforcement learning increase, the agent model has more knowledge of the fully information, which reduces the computing cost of the system. The experimental results show that the reinforcement transformer structure has achieved a certain improvement in speed.
Human-Machine Interactive Tissue Prototype Learning for Label-Efficient Histopathology Image Segmentation

Deep learning have greatly advanced histopathology image segmentation but usually require abundant annotated data. However, due to the gigapixel scale of whole slide images and pathologists’ heavy daily workload, obtaining pixel-level labels for supervised learning in clinical practice is often infeasible. Alternatively, weakly-supervised segmentation methods have been explored with less laborious image-level labels, but their performance is unsatisfactory due to the lack of dense supervision. Inspired by the recent success of self-supervised learning, we present a label-efficient tissue prototype dictionary building pipeline and propose to use the obtained prototypes to guide histopathology image segmentation. Particularly, taking advantage of self-supervised contrastive learning, an encoder is trained to project the unlabeled histopathology image patches into a discriminative embedding space where these patches are clustered to identify the tissue prototypes by efficient pathologists’ visual examination. Then, the encoder is used to map the images into the embedding space and generate pixel-level pseudo tissue masks by querying the tissue prototype dictionary. Finally, the pseudo masks are used to train a segmentation network with dense supervision for better performance. Experiments on two public datasets demonstrate that our method can achieve comparable segmentation performance as the fully-supervised baselines with less annotation burden and outperform other weakly-supervised methods. Codes are available at https://github.com/WinterPan2017/proto2seg .
Education Platforms in Pandemic Conditions

Purpose: The paper aims to describe some educational platforms that were used during the COVID-19 pandemic and analyze their advantages and disadvantages. Methodology: In the research, the authors applied the analysis and generalization of scientific literature on the research topic, processing and interpretation of the information obtained, and the method of system analysis. Educational electronic platforms were used as the research object, developed by foreign and Russian specialists. Findings: It is determined that the necessity of distance learning is urgently growing due to the conditions of COVID-19, which has spread worldwide. The universities have to isolate students and teachers and continue the learning process, which could be reached using various education platforms. Originality/values: Based on the conducted analysis of the advantages and disadvantages of e-learning and studying on different platforms, the authors can admit that it is possible to continue the educational process and achieve some positive results even at the time of forced isolation.
Rethinking the meaning of child-centered and play-centered curriculum in the national kindergarten curriculum using text mining analysis

Objectives This study aimed to rethinking the meaning of child-centered and play-centered curriculum, which are continuously emphasized in the national kindergarten curriculum, focusing on keywords related to ‘child-centered’ and ‘play-centered’. Methods The national kindergarten curriculum and curriculum manual were collected to investigate the meaning of a children-centered and play-centered curriculum. Text mining analysis was conducted through four stages using R. In step 1, the data collection and analysis units were planned. In step 2, pre-data manipulation was performed for data analysis. In step 3, keyword analysis and co-occurrence network analysis were conducted. Finally, in step 4, the analysis results were visualized. Results First, I analyzed the meaning of ‘child-centered’ curriculum by focusing on prenominal expressions that describe child-centeredness, such as ‘experience,’ ‘interest,’ and ‘spontaneity,’ and found that the degree centrality was high for the words child, interest, and experience, and teachers which represent educational targets and curriculum operators. The betweenness centrality indicated a strong emphasis on children's experiences, and the closeness centrality revealed a strong emphasis on activities. Next, I analyzed the meaning of ‘play-centered’ curriculum by focusing on the word ‘play’, and found that the degree of centrality was strong for the words education and activity, the betweenness centrality was prominent for the word interest, and the closeness centrality was evident for the words teacher and teacher's play support in relation to play-centered curriculum and operation. Conclusions This study will contribute to the future revision of the national kindergarten curriculum by providing a conceptual overview for clarifying the terms child-centered and play-centered.