Reviewerly/exHarmony huggingface dataset:
Mimicry API Gateway Decision Algorithm Based on Trust Distribution

Abstract Aiming at the lack of dynamic and comprehensiveness of trust evaluation methods in current adjudication algorithms, an adjudication algorithm for mimic API gateway based on trust distribution is proposed. The algorithm comprehensively considers the direct and indirect trust relationships between equipment executives from the perspective of trust distribution, and on this basis introduces a dynamic penalty strategy, which greatly improves the dynamics and effectiveness of the algorithm. In addition, the algorithm is simulated and tested in the simulated API gateway environment, and the simulation results show that the proposed algorithm has significantly improved the reliability and security of trust evaluation compared with the previous adjudication algorithms.
The Robots are Coming

LG Motion is part of the robotic revolution, with the arrival of a collaborative robot or cobot.
Information Literacy and Sustainable Development Goals Implementation: The Role of Libraries and Librarians

This paper is an exploratory investigation on Information Literacy and Sustainable Development Goals Implementation: The Role of Libraries and Librarians. It is imperative to note that libraries and librarians can help streamline services that encourage the participation and acceptance of stakeholders through its sharing, collaboration and facilitation platforms and services. The familiarity of users to their respective libraries avails the library the opportunity to sensitize, recognize and make visible prospects and challenges recorded with the implementation of SDGs. As it was pointed out that every sector depends on the library, it could be concluded that the library has all that is required to support the sustainable development goals to be achieve because of its characteristic dependability and inclusiveness. This paper recommended that management of libraries should not see the libraries as avenue of enriching themselves as there have been situations where fund released by government has been misappropriated and embezzled at the detriment of effective service provision. There should be in a existence a body with integrity whose responsibility should be to monitor and audit every library project and anyone found culpable punished accordingly. Furthermore, librarians and libraries should get involve in advocacy programmes such as road-walk, distribution of informative flyers, radio jingles and social media campaign with a view to intimating the public on the SDGs.
Object Detection in the Image Recognition Process Using Transformers

Modern object detection methods in the image recognition process us-ing transformer technology are analyzed.The various methods advantages and disadvantages are identified. An own network was created based on the DETR transformer from the FAIR team, and its operation was analyzed. A comparison of the transformer networks perfor-mance with optimized architectures of convolutional neural networks is made.The cloud computing tools, graphics processors, Internet of Things clusters or embedded microprocessor systems were used in the research process.To ensure high object detector accuracy and real-time detection results on different types of devices, an efficient object detector and model scaling technique are required.The transformer model learning is illustrated step-by-step process.
Approximate Reasoning for Large-Scale ABox in OWL DL Based on Neural-Symbolic Learning

The ontology knowledge base (KB) can be divided into two parts: TBox and ABox, where the former models schema-level knowledge within the domain, and the latter is a set of statements of assertions or facts about instances. ABox reasoning is a process of discovering implicit knowledge in ABox based on the existing KB, which is of great value in KB applications. ABox reasoning is influenced by both the complexity of TBox and scale of ABox. The traditional logic-based ontology reasoning methods are usually designed to be provably sound and complete but suffer from long algorithm runtimes and do not scale well for ontology KB represented by OWL DL (Description Logic). In some application scenarios, the soundness and completeness of reasoning results are not the key constraints, and it is acceptable to sacrifice them in exchange for the improvement of reasoning efficiency to some extent. Based on this view, an approximate reasoning method for large-scale ABox in OWL DL KBs was proposed, which is named the ChunfyReasoner (CFR). The CFR introduces neural-symbolic learning into ABox reasoning and integrates the advantages of symbolic systems and neural networks (NNs). By training the NN model, the CFR approximately compiles the logic deduction process of ontology reasoning, which can greatly improve the reasoning speed while ensuring higher reasoning quality. In this paper, we state the basic idea, framework, and construction process of the CFR in detail, and we conduct experiments on two open-source ontologies built on OWL DL. The experimental results verify the effectiveness of our method and show that the CFR can support the applications of large-scale ABox reasoning of OWL DL KBs.
Hubungan Kerja Kelompok Dengan Hasil Belajar Keterampilan Berbicara Mahasiswa Program Studi Pendidikan Bahasa Jerman

The purpose of this study was to describe the relationship between teamwork and learning outcomes of speaking skills. This type of research is descriptive quantitative using correlational analysis techniques and analyzed using product moment correlation. The research was conducted at the German Language Education Study Program, FKIP UNPATTI. The sample in this study were 30 Students of 4th semester. The data analysis technique used is a Likert Scale to measure the attitudes, opinions, and perceptions of respondents towards teamwork with a Likert scale. The results showed that the frequency of presentation of the results of the highest teamwork questionnaire score was 88 and the lowest score was 79. Then the results of speaking skills obtained the highest score of 100 and 18.75 for the lowest score. Furthermore, it is obtained that r is higher than rtable. Based on the results of the study, the hypothesis of this study can be accepted, namely ; there is a positive relationship between teamwork and learning outcomes of German speaking skills
Finding Hidden Patterns in High Resolution Wind Flow Model Simulations

Wind flow data is critical in terms of investment decisions and policy making. High resolution data from wind flow model simulations serve as a supplement to the limited resource of original wind flow data collection. Given the large size of data, finding hidden patterns in wind flow model simulations are critical for reducing the dimensionality of the analysis. In this work, we first perform dimension reduction with two autoencoder models: the CNN-based autoencoder (CNN-AE) [1], and hierarchical autoencoder (HIER-AE) [2], and compare their performance with the Principal Component Analysis (PCA). We then investigate the super-resolution of the wind flow data. By training a Generative Adversarial Network (GAN) with 300 epochs, we obtained a trained model with $$2\times $$ resolution enhancement. We compare the results of GAN with Convolutional Neural Network (CNN), and GAN results show finer structure as expected in the data field images. Also, the kinetic energy spectra comparisons show that GAN outperforms CNN in terms of reproducing the physical properties for high wavenumbers and is critical for analysis where high-wavenumber kinetics play an important role.
PENERAPAN APLIKASI E-COMMERCE BERBASIS BUSINESS TO CONSUMERS PADA TOKO CANADA MENGGUNAKAN FRAMEWORK CODEIGNITER

Toko Canada is a company that sells various kinds of fashion needs for men and women. The system used in sales activities is still manual, which is still using notebooks in processing the data. So in carrying out sales activities is still difficult and takes a lot of time. There needs to be a sales system to facilitate sales activities. Business To Consumers is one of the e-commerce models, which is a business process where sales deal directly with buyers. Buying and selling directly through word of mouth is still done in the era of technological developments like now. However, the buying and selling process was deemed less efficient and the scope of sales was felt to be less extensive. So that these problems are not sustainable, an e-commerce business model website will be built for customers. In building an e-commerce system, the author uses the prototype method by designing the system using the Unified Modeling Language (UML). Then MySQL supported to manage existing databases. The final result of this research is an e-commerce system at Toko Canada that can help and facilitate various sales activities. The conclusion of this research is that the sales application that has been made makes it easy for owners to get accurate reports and can also help employees to record reports.
Noun-phrase complexity in the texts of intermediate-level Norwegian EFL writers: stasis or development?

This paper examines the longitudinal development in noun-phrase complexity in English texts written by Norwegian learners in school years 8-10. The study is based on material from the TRAWL (Tracking Written Learner Language) corpus (Dirdal et al., 2017; Dirdal et al., 2022), comprising longitudinal data from nine pupils. The study tests the hypothesis proposed by Biber et al. (2011) that noun-phrase complexity increases gradually as writers develop, and answers the following research question: To what extent can longitudinal development in noun-phrase complexity in accordance with Biber et al.’s (2011) stages be traced in the written production of intermediate-level Norwegian EFL writers in Years 8-10? The results indicate that there is very little evidence to suggest an increase in sophistication as regards phrasal modification over the three years. Instead, the pupils primarily rely on the types of modifiers that are acquired in early developmental stages, such as attributive adjectives and prepositional phrases. Thus, there should be a greater pedagogical focus on developing pupils’ skills in using more sophisticated noun-phrase modifiers, to prepare them for the future demands placed on their academic-writing skills.
Combination of Ant Colony Tabu Search Algorithm with Firefly Tabu Search Algorithm (ACTS-FATS) in Solving the Traveling Salesman Problem (TSP)

Traveling Salesman Problem (TSP) is a classic combinatorial optimization problem, one of the optimization problems that can be applied to various activities such as finding the shortest path. The optimization problem in TSP is the most widely discussed and has become the standard for testing computational algorithms. TSP is a good object to test optimization performance. With scientific developments in the field of informatics, many researchers have optimized the application of algorithms to solve the Traveling Salesman Problem (TSP). In this study, researchers used a combination of Ant Colony Tabu Search – Firefly Algorithm Tabu Search (ACTS-FATS). The combination is doneto overcome Premature Convergence (trapped local optimum) which is a shortcoming of the ant colony algorithm, get the best running time by looking at the process of each point movement with the ant colony and firefly methods. After testing, getting the best running time results of 27.79%, and getting an accuracy rate of 17%.
AUTOMATED SAFE FOR FIREARMS STORAGE: AN EFFECTIVE SOLUTION FOR SECURITY COMPANIES

With the growing articulation of organized crime and the increase in violence, manual methods become insufficient to effectively control firearms by companies, requiring the use of multiple technologies to guarantee a higher level of security in the custody of this material.The present work describes a firearm control system characterized by locking the firearm in the safe in an automated way, making it impossible to remove it without prior authorization.The purpose of this system is to prevent human error and lack of security from allowing the diversion of stored firearms.Additionally, this work aims to research patents for similar inventions in order to verify the potential of this system.The applied methodology consists of a steel safe with an electromagnetic pin lock that passes through the trigger key and the trigger guard of the firearm.The removal and storage control of the weapon in the safe is performed by a PLC (Programmable Logic Controller) in communication with a computer that opens the safe and unlocks the weapon lock.The results of this work were compared with similar patents already registered and showed an innovative character, a totality of functions and a higher level of security.
A Novel Approach for Weakly Supervised Object Detection Using Deep Learning Technique

Computer vision has enormous potential in understanding useful representations and patterns from images and videos so that it helps to make intelligent decisions in a similar manner as humans learn from their surroundings. There has been a significant advance in the field of computer vision application: object detection. It recently came into prominence due to the emergence of deep learning techniques. Object detection is applied in wide areas of applications, like crowd detection, face detection, medical image analysis, video analysis, military applications, self-driving cars, etc. This chapter focuses on the latest advancements in object detection using deep learning techniques. These object detection techniques can be used to solve the problem of solid waste material detection so as to maximize the recycling process and automate the process of waste segregation. The well-known problem of object detection is to train the deep learning model for object classification using object-level annotations which is more time consuming and expensive. This chapter helps to provide the solution for solid waste material detection by using weakly supervised object detection. It works on image-level annotations only. This would be useful to locate recyclable items from the multiple objects present in a scene. Pre-trained deep learning models are used for feature extraction of input images which is an essential step before actual object detection tasks. Finally, promising future directions are provided for researchers.
Optimized Modified K-Nearest Neighbor Classifier for Pattern Recognition

In this chapter, we proposed an optimized version of modified K-NN (MKNN) proposed by us, to reduce the number of group prototypes created within a pattern class, for compact knowledge representation. A scalar distance factor 0 ≤ d ≤ 1 was used in MKNN to create group prototypes. But, in the proposed approach (OMKNN), we suggest to use a vector of, possibly, different distance parameters, one for each pattern class. This distance vector can optimally reduce the number of group prototypes created per class. As per our experimentation using machine learning data sets, we found that proposed OMKNN creates less number of group prototypes and thus needs less recall time per pattern along with improved recognition accuracy. Using the proposed OMKNN, we obtained a 33% reduction in group prototypes and hence the space, with the increase in recognition accuracy. Thus, a difficult goal to reduce both the space and recall time per pattern along with increased recognition accuracy is achieved.
Web-Based Caricature Sales Information System Using the FAST Method

The use of information technology has become widespread throughout all sectors of society, including government, business, healthcare, and education. Dalis Art Medan is a digital art-focused online store that opened its doors in 2017. Dalis Art's current sales system still relies on Facebook and Instagram because it does not yet have e-commerce, which results in low sales. Reporting mistakes are common since Dalis Art sales transaction data is still manually entered. E-commerce has been suggested as a solution to Dalis Art's issue with developing its sales operation. The FAST (Framework for the Application of Systems Thinking) methodology is used in this study. The FAST technique incorporates system users more actively in each stage of system development, is more adaptable, can be produced with other methods, and can be customized to the author's standard demands. The goal of this study is to make it easier for customers to learn about and purchase products from Dalis Art. Benefits might give consumers quick access to information on the items in Dalis Art. The study's findings demonstrate how fast and accurately a web-based sales information system, including details on products, ordering procedures, and payments, can be accessible via this method.
Application of Naïve Bayes Algorithm for Non-Cash Food Assistance Recipients in Kampar Regency

Non- Non-Cash Food Assistance (BPNT) is a non-cash food social assistance from the government given to beneficiary families (KPM) of Rp. 200,000 per month which is given in the form of basic necessities by using an electronic card. The large number of residents who will be selected makes it difficult for village officials to make decisions on who is eligible or ineligible as recipients of non-cash food assistance every month. This research was conducted to design a decision support system for the eligibility of non-cash food assistance recipients by using the naïve bayes algorithm in order to help the Parit Baru Village apparatus every month in determining the eligibility of the next non-cash food assistance recipients, by perform Confusion Matrix calculations. From the results of the discussion carried out, it can be concluded that Naïve Bayes and the resulting rules had an accuracy rate of 95%, the Precision value was 94% and Recall was 100%. Therefore, the Naïve Bayes algorithm can be applied to the decision support system in determining the eligibility of recipients of non-cash food assistance.
Improve steganography system using agents software based on statistical and classification technique

In digital communications, information security is a paramount necessity. In the hiding algorithm, there are three basic parameters: security, capacity, and imperceptibility. Therefore, there are many ways to design the steganography algorithm, such as least significant bit (LSB), discrete wave transformation (DWT), and discrete cosine transform (DCT). The aim of this paper is to improve agent software design based on a steganography system. It proposed an agent system based on a support vector machine (SVM) classifier to hide a secret message in a certain cover image. The common dataset for steganography uses 80% training and 20% testing to get accurate results. Developing an agent system depends on six statistical parameters such as energy, standard deviation, histogram, variance, mean, and entropy. This resulted in features classified by the SVM classifier to predict the best cover image to be nominated for embedding. Worthy results were obtained in terms of imperceptibility, attack, and cover image prediction by statistical issues.
Respiratory rhythm affects recalibration of body ownership

Change in body perception requires recalibration of various sensory inputs. However, it is less known how information other than sensations relates to the recalibration of body perception. Here, we focused on the relationship between respiration and cognition and investigated whether respiratory rhythms are related to the recalibration of hand perception. We built a visual feedback environment, in which a mannequin hand moved in conjunction with its own respiratory rhythm, and participants performed an experiment under conditions in congruency/incongruency for spatial and temporal factors. The temporal and spatial congruency between own respiratory rhythm and the mannequin hand markedly facilitated the phenomenon of hand ownership sense transfer to the mannequin hand, while incongruency had little effect on the change in hand ownership. The finding suggests that an internal model in the brain allows respiratory rhythms to be involved in the adaptation of the body's neural representations.
Computational Simulations of Fabrication of Aluminum-Based Josephson Junctions: Topological Aspects of the Barrier Structure

Although the performance of qubits has been improved in recent years, the differences in the microscopic atomic structure of the Josephson junctions, the core devices prepared under different preparation conditions, are still underexplored. In this paper, the effects of the oxygen temperature and upper aluminum deposition rate on the topology of the barrier layer in the aluminum-based Josephson junctions have been presented by classical molecular dynamics simulations. We apply a Voronoi tessellation method to characterize the topology of the interface and central regions of the barrier layers. We find that when the oxygen temperature is 573 K and the upper aluminum deposition rate is 4 Å/ps, the barrier has the fewest atomic voids and the most closely arranged atoms. However, if only the atomic arrangement of the central region is considered, the optimal rate of the aluminum deposition is 8 Å/ps. This work provides microscopic guidance for the experimental preparation of Josephson junctions, which helps to improve the performance of qubits and accelerate the practical application of quantum computers.
Sosialisasi dan pelatihan aplikasi Sistem Informasi Data dan Pembayaran Iuran Bulanan warga

Apart from being in the general community group, residents are also in a complex residential environment. There are housing complexes for the community in subsidized and commercial housing. The large number of citizen data in one housing complex raises many problems, both harmony problems, and administrative problems. The most fundamental problem is how the system records valid citizens and whether the contribution payment system is accountable, valid, and transparent. The team of lecturers from Hamzanwadi University's Informatics Engineering Study Program is doing this service to help the management of RT 005 Lingkar Pratama build a free application system and collect data from citizens. So, it can help RT administrators record and manage complex residential residents' contributions. The method in this service is carried out in stages, starting with problem data analysis, socialization, and training. The results of this service are in the form of implementing applications that can already be used and assist RT admin officers in managing financial reports and information management of citizen data. Of the fourteen administrators who attended the training, on average, they had mastered the application
Long distance entanglement and high-dimensional quantum teleportation in the Fermi–Hubbard model

Abstract The long distance entanglement in finite size open Fermi–Hubbard chains, together with the end-to-end quantum teleportation are investigated. We show the peculiarity of the ground state of the Fermi–Hubbard model to support maximum long distance entanglement, which allows it to operate as a quantum resource for high fidelity long distance quantum teleportation. We determine the physical properties and conditions for creating scalable long distance entanglement and analyze its stability under the effect of the Coulomb interaction and the hopping amplitude. Furthermore, we show that the choice of the measurement basis in the protocol can drastically affect the fidelity of quantum teleportation and we argue that perfect information transfer can be attained by choosing an adequate basis reflecting the salient properties of the quantum channel, i.e. Hubbard projective measurements.