Reviewerly/exHarmony huggingface dataset:
Orthogonal Embedding Hashing with Semantic Reinforcement for Modal Matching

Cross-modal hashing (CMH) is a fast modal association technique in multimodal learning community. Current CMH techniques, however, are susceptible to scaling in binarization, resulting in semantic deficiency of hash codes. It is mainly caused by the redundancy of raw features and the loss of binary quantization. For these occasions, a novel orthogonal embedding hash with semantic reinforcement (SR-OEH) is proposed to alleviate these limitations. To be specific, SR-OEH firstly leverages the associated tags to guide feature semantic condensation by optimized adjustment weights, which are trained by maximizing the sample edge, so as to highlight the relevant features with detrimental redundancy degradation. What's more, we construct an efficient iterative optimization algorithm to learn a strong semantic hash dictionary by supervised rotation factor step by step orthogonal, so as to effectively offset the quantization loss induced by the mixed integer optimization. Finally, two common hash functions are employed to concatenate the hash book and the deepened semantic features, thus accomplishing cross modal matching. The promising results obtained on five public data sets bear out the superiority of proposed strategy against the representative state-of-the-art cross-modal hashing baselines.
Analysis of the Implementation of ISO 15489:2016 Record Management at Bank Indonesia Representative Offices

ISO 14598 has a national standard in archive management so that agencies can have a more organized procedure or system for handling archival documents. The purpose of this study is to find out how the implementation of ISO 15489: 2016-Record Management and find out the obstacles in Processing for Creating, Capturing, and Managing Records at Bank Indonesia, Bengkulu province. This research uses qualitative methods, data taken from the results of systematic observations, in-depth interviews with informants. The results of the study stated that Bank Indonesia had received a complaint of ISO 15489: 2016 but in its implementation there were still problems in filing and depreciation of archives. To respond to this problem, Bank Indonesia can check the BI-RMS application regularly so that it knows which files can be moved and destroyed, and archivists must record the files issued by each unit to avoid losing archives.
Intrinsic Lexical Intentionality and the Mathematics of Homomorphism

Moisl [1, 2] proposed a model of how the brain implements intrinsic intentionality with respect to lexical and sentence meaning, where 'intrinsic' is understood as 'independent of interpretation by observers external to the cognitive agent'. The discussion in both was mainly philosophical and qualitative; the present paper gives a mathematical account of the distance structure preservation that underlies the proposed mechanism of intrinsic intentionality. The three-layer autoassociative multilayer perceptron (aMLP) architecture with nonlinear hidden and linear output layers is the component in the model which generates representations homomorphic with the environment. The discussion first cites existing work which identifies the aMLP as an implementation architecture for principal component analysis (PCA), and then goes on to argue that the homomorphism characteristic of linear functions like PCA extends to aMLPs with nonlinear activation functions in the hidden layer. The discussion is in two main parts: the first part outlines the model, and the second presents the mathematical account.
Digital Repositories and Discoverability

Digital repositories typically improve the efficacy with which GLAMR organizations can manage their digital content and ensure its discoverability.Repositories are consequently a key enabling technology and have come to dominate the digital service offerings of many organizations.This chapter will provide a background to digital repositories, their emergence, and their typical characteristics.We will first seek to define digital repositories and will go on to explore several prominent repository types, resulting in the presentation of a 'digital repository typology'.The concept of discoverability is central to our understanding of repositories and is a core concern of this chapter too.We will therefore explore the principal attributes of repository discoverability, including consideration of the most significant digital repository discoverability technologies and users' typical routes to repository content discovery.
Application of Obligatory Prayer Learning Based on Augmented Reality

Prayer is a physical, mental, and spiritual activity that gives good meaning to relationships with Allah, fellow human beings, and oneself. Prayer begins with ablution first to remove najis and hadats. Religion is a rule, guideline, teaching, or system that governs beliefs, beliefs, or beliefs. Islam was revealed by Allah SWT. In Islam, expressing gratitude for every Muslim to the Creator is by praying. Moreover, at this time, the current guidance book on prayer procedures is still running in the delivery of information in the form of text and 2D images. This way is not valuable because it is not mobile style, while currently, human mobility is higher. In this study, we are designing and building a learning application of obligatory prayer based on augmented reality to run on Android-based smartphones and can be studied anywhere and anytime without being limited by space and time. This application contains procedures for ablution and movements of people praying in the form of 3D animation and audio.
Comparison of SAW, WP, and TOPSIS Methods in Determining the Best Journalists

Journalists are human resources that have a significant influence on journalistic companies. A system is needed to support the company's decision to select and measure its reporters. PT. Inipasti Communika is one of the journalistic companies that has never previously measured and assessed its journalists, so it has difficulty assessing and measuring its journalists. This study aims to provide a solution using the Decision Support System in decision-making using the SAW, WP, and TOPSIS methods and provide the final decision results based on comparing these methods. This study uses criteria and criteria values from these companies. The company's data related to its journalists is the privacy of PT. It is a Community, so the alternative value used is dummy data that is still by the original standards of the company's data. This study concludes that the three methods can provide the best alternatives with the same results.
Video Object Segmentation with Comprehensive Scene Awareness

This paper aims to investigate the video object segmentation task (VOS) in semi-supervised settings, which estimates the mask of the target object in subsequent frames based on its mask in the initial frame. Currently, the most advanced methods primarily depend on generative target appearance models, but as they are susceptible to background distractors resembling the target objects, only limited robustness can be achieved. To eliminate this influence, a semi-supervised VOS method with comprehensive scene awareness is proposed in this study, which consists of a target appearance network and a scenario knowledge network. The target appearance network adopts a memory-based architecture to obtain the appearance cues of target objects. The scenario knowledge network captures detailed environmental cues, such as the position of distractor objects, which greatly assists in locating the target. To ensure that the scenario knowledge is learned accurately and effectively combined with the output of the target appearance network, a union loss function is designed to supervise the network model for training. On several prevalent benchmarks, this method achieves a favorable performance compared to the most advanced methods currently available. The scenario knowledge network contains complementary information and provides a considerable performance increase, even when considering a strong appearance model.
Privacy-Preserved Neural Graph Similarity Learning

To develop effective and efficient graph similarity learning (GSL) models, a series of data-driven neural algorithms have been proposed in recent years. Although GSL models are frequently deployed in privacy-sensitive scenarios, the user privacy protection of neural GSL models has not drawn much attention. To comprehensively understand the privacy protection issues, we first introduce the concept of attackable representation to systematically characterize the privacy attacks that each model can face. Inspired by the qualitative results, we propose a novel P rivacy-P reserving neural G raph M atching network model, named PPGM, for graph similarity learning. To prevent reconstruction attacks, the proposed model does not communicate node-level representations between devices. Instead, we learn multi-perspective graph representations based on learnable context vectors. To alleviate the attacks to graph properties, the obfuscated features that contain information from both graphs are communicated. In this way, the private properties of each graph can be difficult to infer. Based on the node-graph matching techniques while calculating the obfuscated features, PPGM can also be effective in similarity measuring. To quantitatively evaluate the privacy-preserving ability of neural GSL models, we further propose an evaluation protocol via training supervised black-box attack models. Extensive experiments on widely-used benchmarks show the effectiveness and strong privacy-protection ability of the proposed model PPGM. The code is available at: https://github.com/RUCAIBox/PPGM.
Gait Control Applications on Four Legged Robot

A common problem faced by four-legged robots is when they encounter obstacles that have structures that force the robot to lift its legs higher than the robot's ability limit and can disrupt the robot's balance, so it is necessary to add a gait control method. This research has resulted in controlling the movement of a four-legged robot using the wave gait control method to pass obstacles in the form of stairs and uneven floors, where the movement of the robot by lifting one leg at a time makes the balance of the robot very awake. So that the robot can make decisions if the robot encounters obstacles in the form of stairs and uneven floors, the role of the ultrasonic sensor is very decisive in reading the environment. The way for the robot to climb stairs and not fall backwards is to slide the robot's hind legs back so that each robotic footstep will form a triangle. When the robot detects the presence of a stairs, the robot will lift its legs by 50o, while when it detects an uneven floor, the robot's legs will lift its legs by 40o. Based on the results of research conducted with 4 experiments, namely the success of the robot in passing through the trajectory without obstacles by 100%, the success of the robot in crossing the uneven floor by 60%, the success of the robot in passing the stairs by 50%, while the success of the robot in passing the stairs and the uneven floor by 70%.
Impact of Parallel vs. Non Parallel Corpora on the Identification of Arabic Dialects

In this paper, we conduct a study to evaluate the performance of statistical and neural methods to classify Arabic Dialects (AD). This evaluation is based on two kinds of corpora. The first one is a corpus named PADIC (Parallel Arabic DIalectal Corpus), which is a multi-dialectal corpus composed of six dialects: two Algerian dialects (of Algiers and Annaba cities), Palestinian, Syrian, Tunisian, and Moroccan, in addition to MSA. The second one (AraDial) is a manually collected corpus that contains the same dialects as well as the same number of sentences as PADIC (6412 sentences for each dialect). In our experiments, we used both statistical and neural classifiers, namely: Gaussian Naive Bayes, Bernoulli Naive Bayes, kNN, Logistic Regression, SGD Classifier, Passive Aggressive Classifier, Perceptron, Linear Support Vector, and Convolutional Neural Network Classifiers. We evaluated these classifiers in two setups: training on a parallel corpus (PADIC) and testing on the non-parallel corpus and vice versa. The obtained results have shown that training our system on a non-parallel corpus will give better results, as we achieved a mean score of 92.08\%.
Assessment of Heavy Metals Concentrations in Some Irrigated Soils of Dadin-Kowa Dam, Gombe State, Nigeria

This study assessed heavy metals concentration in some irrigated soils of Dadin Kowa Dam, Gombe State Nigeria. The soils were collected along transect lines (different sampling points) within the farms and samples were taken to the laboratory for analysis. The analysis was conducted using (Thermo Fisher Scientific) Energy Dispersive X-ray Fluorescence Spectroscopy. The results give the following metals with their mean concentrations: Mo (8.200 and 8.926), Zr (432.990 and 440.236), Sr (204.358 and 87.142), Rb (47.642 and 42.533), Zn (44.165 and NA), Cu (42.161 and NA), Fe (43090.640 and 24846.236), Mn, (992.881 and 397.248), Cr (140.456 and 88.054), V (56.842 and 27.159), Ti (2403.982 and 1421.486), Ca (3344.789 and 982.722), K (4706.758 and 3172. 047), S (466.152 and 310.510), Ba (933795.090 and 959675.238) and Nb (26.285 and 24.964) were obtained from the onion and cabbage farmssoils samples respectively (Values in part per millions (ppm)). The degree of pollution of the soils studied were ascertained and compared with literature toxicity data. Other pollution indices like Enrichment factor, Clarke ratio, Coefficient of variation, Contamination factor, Metal Pollution Index and Geo- Accumulation Index were assessed also and found out that the contamination level for the soils is tolerable for irrigation. At α = 0.01 significant level, positive correlation exists between Fe and Cu, Zn and Ca while Zn and Rb; Ca and Rb have significantly negative correlation between them. By implications, these results showed that the complexes of some elements within the soil strata could influence the presence of other given elements. Therefore, Dam irrigation area soils studied showed relatively low state of pollution for the elements determined.
SOFTWARE ARCHITECTURE FOR MATHEMATICAL MODELING BASED ON INTERVAL AND ONTOLOGICAL APPROACH

Mathematical knowledge is a term often used to describe various components of mathematical science, such as theorems, lemmas, axioms, proofs, etc.. By the term “knowledge from mathematical modeling” we understand the definition of a huge amount of knowledge, which is formalized both within the framework of a specific mathematical model and in the process of its construction, as well as related procedures of practical use. Compared to other forms of knowledge, such as rules, solution trees, mathematical knowledge is more abstract and more structured. In general, the ontological approach to mathematical modeling refers to the use of ontology, as it is based on the terms of mathematical modeling and descriptions of relationships between the main processes of its course.. The general ontological approach to mathematical modeling from an applied point of view does not allow to manage the processes of building mathematical models, because it can only be used to describe the area of mathematical modeling. From an applied point of view, most basic forms of mathematical knowledge are either embedded in specific software tools, such as models of aggregate operation in simulation software, or must be formally interpreted into a more general mathematical tool, following appropriate syntactic rules. Most of this type of knowledge relates to specific modeling tasks and is clearly implemented with the help of appropriate procedural descriptions, rather than declarative representations, unlike the philosophical vision of mathematical modeling. The paper substantiates the use of the ontological approach as an effective tool for managing the processes of building mathematical models based on interval data and using these models for applied problems. The use of the ontological model made it possible to formalize the process of obtaining, storing and using knowledge obtained in the process of mathematical modeling. The article also presents the features of building a software architecture for mathematical modeling based on interval analysis and an ontological approach. The technology for creating software based on the developed ontological add-on for mathematical modeling using interval data for various objects, as well as various forms of user interface implementation, is described. A number of diagrams illustrating the peculiarities of using the ontological approach based on interval data are presented and the peculiarities of its interpretation in applied fields, in particular, in the tasks of environmental monitoring, are described.
The Effect of Cooking Class on the Cognitive Abilities of Group a Kindergarten Children in Cluster IV Gedangan Sidoarjo District

This study aims to determine the effect of cooking class activities on the cognitive abilities of group A children in Cluster IV, Gedangan Sidoarjo District. This study uses a quasi-experimental design through a quantitative approach. The experimental design used was nonequivalent control group design. The subjects in this study were the children of group A RA Al Islah Karangbong with a total of 51 children as the experimental group and PAUD Tashwirul Afkar Karangbong with a total of 30 children as the control group. Data analysis techniques in this study used non-parametric statistics with the Mann Whitney Test. The results of this study indicate that "there is an effect of cooking class activities on the cognitive abilities of Group A kindergarten children in Cluster IV Gedangan Sidoarjo District" with a statistical value of Z = -4.076 with a significance level of 0.000 or less than 5%. This shows that there is an effect of cooking class activities on the cognitive abilities of Group A kindergarten children. Cooking class activities can be used as an interesting and fun learning reference for children in improving children's cognitive abilities because children get direct experience.
Staffing Data Processing at Universitas Sawerigading

Personnel data management through the personnel management information system is all process activities carried out by an operator or an administrator in a WEB-based application that is designed as a solution to handle various matters in managing personnel data. Given the importance of managing employee data, improving the quality of personnel data management through a system is one of the priorities in the e-Government development stage. Good employee management will improve employee performance in an agency. This study aims to determine the processing of personnel data through information systems at Universitas Sawerigading. This research uses quantitative method with descriptive research type. The respondents in this study were 35 people. Data was collected using observation, questionnaires and documentation techniques. The data analyzed is the data obtained based on the respondents' answers. The results showed that the staffing data processing was in a fairly good category. From the aspect of data collection, data management, data storage and data reporting.
Stacking Deep-Learning Model, Stories and Drawing Properties for Automatic Scene Generation

Text-image mapping is of great interest to the scientific community, especially for educational purposes.It helps young learners, mainly those with learning difficulties, to better understand the content of stories.In this paper, we propose to capture the teacher's experience in manually building relevant scenes for animal behavior stories.This manual work, which consists of a pair of texts and a set of elementary images, is fed into a Long Short-Term Memory (LSTM) followed by a Conditional Random Field (CRF) that aims to associate the relevant words in the text with their corresponding elementary image while preserving the drawing properties.This association is then used for scene construction.Several experiments were conducted to show how better the constructed scenes convey textual information than the scenes constructed from the competitor's models.
Service Reliability Evaluation of Highway Tunnel Based on Digital Image Processing

China is gradually transitioning from the "tunnel construction exploration era" to the "tunnel high-quality construction and operation era", and the maintenance demand of highway tunnels has increased sharply. Therefore, there is an urgent need for an evaluation method to evaluate the service reliability of highway tunnels, so as to provide reference for tunnel maintenance personnel to carry out maintenance work. Taking highway tunnels as the research object, this paper extracts three parameters, including length, maximum width and fractal dimension, from the binary image of highway tunnel lining cracks. The standard for dividing the length of the highway tunnel section is 500m as the tunnel section, and a section disease sample space including multiple highway tunnels is constructed. The EM clustering algorithm was used to determine the number of graded grades of disease, and the relative Euclidean distance was used as the evaluation index to divide the safety grade of the tunnel into five grades: normal, degraded, degraded, deteriorated and dangerous. The partial least squares method is used to establish the lining service reliability evaluation formula and verify the residual of each sample point in the sample space. The smaller the average value of the residual, the better fitting effect of the established evaluation formula. The service reliability evaluation method proposed in this paper is applied to engineering practice and compared with the expert scoring method and the national standard method, which proves that the evaluation method in this paper has the advantages of strong visibility, simple evaluation method, and is conducive to engineering practice.
Bonding experiences in times of pandemic

The university bonding is very important within the integration of education and society, since it allows interaction between science and the common citizen, in addition to training future professionals with civic awareness, entering them into the social experience as workers. On the other hand, the role of virtual tutorials has come to occupy an important role within the modalities of education and an indispensable tool that allows knowledge to be built through participant leader interaction with the support of information technologies. The objective of this research is to identify the incidence of virtual math tutorials on the calcifications average of public education students in high school. One of the consequences of the COVID-19 pandemic was the fact that not all students had the tools to access their virtual classes, and those who did have access were not adapted to this modality; this led to the creation of knowledge gaps, especially in practical subjects such as mathematics. The results indicate that there was a positive impact on the grade point averages showing an increase in them, so it is concluded that it is very important to identify those subjects that merit accompaniment, and through the implementation of virtual tutors, guide students in the process teaching and learning, especially in practical subjects to improve school performance.