Reviewerly/exHarmony huggingface dataset:
Penerapan Metode Fuzzy Tsukamoto Untuk Penentuan Jurusan Sesuai Minat Bakat Penerimaan Siswa Baru Pada SMK

In recent industrial revolution 4.0 era, work field demands for workers whose character, creative, innovative and competent so that education sector through the educational institution, specifically the Vocational Schools (SMK) is expected to produce competitive graduates in the advancement of recent work field. The graduate of Vocational Schools are demanded to have more work preparedness. Nevertheless, the more option of vocation in a Vocational School had made more complication for new students to choose the existing vocation. The selection of vocation in Vocational School is sometimes not in accordance with their talents and interests. It is to help the students choose the vocation according to their talent and interests. By taking selection on Vocational School of SMK Pancasila 5 Wonogiri as the object of study, the researcher design and develop an application to determine the vocation according to their talents and interest. This application uses fuzzy tsukamoto calculation method in its value processing and also PHP programming language in program code design. The results is the application which is capable to determine a vocation according to students’ talent and interest and also subject value as the calculation reference.
Game Design for Better Security of Combination Locks

Dial locks are commonly used to secure a person’s items. Commercially available dial locks often use four or five wheels of letters, allowing a user to select a word as a combination. In order to evaluate the security of these locks, we create a game, with an instance created by the lock designer, and played by a lock owner and a thief. In the game, the lock owner chooses a word as a combination, and the thief creates a brute force strategy to try all possible combinations that yield words until the combination is found. To accomplish the task, the thief will solve a version of the Probabilistic Travelling Salesman Problem (PTSP) by creating an a priori tour through all the words a lock can create. The goal for the game designer, then, is to create a lock configuration that maximizes the expected length of the best possible PTSP tour. This paper describes a Genetic Algorithm (GA) approach to design a near-optimal game, i.e. a lock configuration that makes it as difficult for the thief to crack. An analysis of the output of the GA shows that the locks that the system creates are significantly more secure than both commercial locks, in the context of this game.
Procedural Text Understanding via Scene-Wise Evolution

Procedural text understanding requires machines to reason about entity states within the dynamical narratives. Current procedural text understanding approaches are commonly entity-wise, which separately track each entity and independently predict different states of each entity. Such an entity-wise paradigm does not consider the interaction between entities and their states. In this paper, we propose a new scene-wise paradigm for procedural text understanding, which jointly tracks states of all entities in a scene-by-scene manner. Based on this paradigm, we propose Scene Graph Reasoner (SGR), which introduces a series of dynamically evolving scene graphs to jointly formulate the evolution of entities, states and their associations throughout the narrative. In this way, the deep interactions between all entities and states can be jointly captured and simultaneously derived from scene graphs. Experiments show that SGR not only achieves the new state-of-the-art performance but also significantly accelerates the speed of reasoning.
Automated Synthesis of Generalized Invariant Strategies via Counterexample-Guided Strategy Refinement

Strategy synthesis for multi-agent systems has proved to be a hard task, even when limited to two-player games with safety objectives. Generalized strategy synthesis, an extension of generalized planning which aims to produce a single solution for multiple (possibly infinitely many) planning instances, is a promising direction to deal with the state-space explosion problem. In this paper, we formalize the problem of generalized strategy synthesis in the situation calculus. The synthesis task involves second-order theorem proving generally. Thus we consider strategies aiming to maintain invariants; such strategies can be verified with first-order theorem proving. We propose a sound but incomplete approach to synthesize invariant strategies by adapting the framework of counterexample-guided refinement. The key idea for refinement is to generate a strategy using a model checker for a game constructed from the counterexample, and use it to refine the candidate general strategy. We implemented our method and did experiments with a number of game problems. Our system can successfully synthesize solutions for most of the domains within a reasonable amount of time.
Metaphors and Similes in Peter Lerangis' Novel The Curse of the King

This study aims to reveal the writing style in the novel The Curse of the King by Peter Lerangis, especially in its figurative speech namely metaphor and simile. The classification of metaphors analyzed uses Newmark's classification and the simile classification used in this study is the classification described by Fromilhague. A qualitative method was used to answer the problem of the study. The research showed that there were thirteen metaphors and fifty-two similes. The metaphor compositions found were one Dead Metaphor, two Recent Metaphors, Three Stock metaphors, seven Original Metaphors, and no Cliche Metaphors. The similes found were five Explicit Similes and forty-seven Implicit Similes. Metaphors and similes in the novel use many popular references such as the words of Donald Trump, party guest, Children of the Corn, Velcro, Ninja, props in a movie, Soprano, Pizza, Flashmob, a dancer on steroids, Looney Tunes, and ET. The combination of past history with a writing style that uses popular figures of speech becomes an interesting mix that makes the novel gain many fans in the world. The novelty of this type of research is still very broad, especially in research on new words in historical fiction such as in the work of Peter Lerangis.
The Importance of Hyperparameter Optimisation for Facial Recognition Applications

This paper explores the importance of using optimisation techniques when tuning a machine learning model. The hyperparameters that need to be determined for the Artificial Neural Network (ANN) to work most efficiently are supposed to find a value that achieves the highest recognition accuracy in a face recognition application. First, the model was trained with manual optimisation of the parameters. The highest recognition accuracy that could be achieved was 96.6% with a specific set of parameters used in the ANN. However, the error rate was at 30%, which was not optimal. After utilising Grid Search as the first automated tuning method for hyperparameters, the recognition accuracy rose to 96.9% and the error rate could be minimised to be less than 1%. Applying Random Search, a recognition accuracy of 98.1% could be achieved with the same error rate. Adding further optimisation to the results from Random Search resulted in receiving an accuracy of 98.2%. Hence, the accuracy of the facial recognition application could be increased by 1.6% by applying automated optimisation methods. Furthermore, this paper will also deal with common issues in face recognition and focus on potential solutions.
Application of Artificial Intelligence In The Detection Of Plant Diseases (Clubroot)

Indonesia is a tropical country with diverse flora and fauna stretching from west to east. One of the cabbage plants grown in Indonesia has many benefits for human health. Cabbage plants are susceptible to diseases such as pests and pathogens. One of them is clubroot disease, which causes the plant not to grow and develop. Based on the above problems, it is necessary to have a system to support farmers in the scientific field of expert systems that uses the Certainty Factor method to diagnose the nature of clubroot disease. It is hoped that this system can provide information more quickly on whether or not cabbage plants have clubroot disease. The results of this study represent an integrated system capable of solving problems in cabbage plants, especially in the diagnosis of clubroot disease. It is hoped that the applied system can be further developed together with technological developments
Improving Zero-Shot Phrase Grounding via Reasoning on External Knowledge and Spatial Relations

Phrase grounding is a multi-modal problem that localizes a particular noun phrase in an image referred to by a text query. In the challenging zero-shot phrase grounding setting, the existing state-of-the-art grounding models have limited capacity in handling the unseen phrases. Humans, however, can ground novel types of objects in images with little effort, significantly benefiting from reasoning with commonsense. In this paper, we design a novel phrase grounding architecture that builds multi-modal knowledge graphs using external knowledge and then performs graph reasoning and spatial relation reasoning to localize the referred nouns phrases. We perform extensive experiments on different zero-shot grounding splits sub-sampled from the Flickr30K Entity and Visual Genome dataset, demonstrating that the proposed framework is orthogonal to backbone image encoders and outperforms the baselines by 2~3% in accuracy, resulting in a significant improvement under the standard evaluation metrics.
Synthesis of Zeolite-NaY for Methane Adsorption

This research aims to synthesize zeolite-NaY material which is then applied as an adsorbent for methane adsorption. Zeolite-NaY was synthesized by hydrothermal method and characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), and isotherm adsorption-desorption N2 (BET). The characterization results showed that synthesized zeolite-NaY had a diffraction peak with the highest intensity at 2θ = 6° and its morphology showed octahedral crystals. Zeolite-NaY was then applied as an adsorbent for methane adsorption. The methane adsorption test on zeolite-NaY was carried out using the gravimetric method at temperature of 30 °C and pressure of 1 bar. The data was recorded of increasing time every 1 minute until constant. The adsorption test results showed that the methane adsorption capacity on the zeolite-NaY material was 0.436 % wt.
The Relationship Between Internal Control And Compensation Appropriateness For Accounting Fraudulent Actions In Retail Companies

The background of this research is how to find accounting fraud in a retail company, which consists of internal control variables and compensation suitability. Therefore, with accounting fraud, it can be prevented from an early age so that retail companies can continue to move forward in the future. The method used in this study is to use the literature review method and use quantitative methods by conducting a survey to 22 retail companies in order to find out frauds that exist in retail companies. The problem in this research is how to find out accounting fraud in a retail company that raises variables from internal control and compensation suitability with the two variables, it will be known what fraudulent actions occurred in retail companies so that retail companies can progress and develop so that they do not the loss is getting worse. The purpose of this study is how to find out accounting frauds in a retail company, because the fraud can make a retail company close and go bankrupt. Therefore, with preventive measures, it can be seen what things can prevent the company from cheating.
DALG: Deep Attentive Local and Global Modeling for Image Retrieval

Deeply learned representations have achieved superior image retrieval performance in a retrieve-then-rerank manner. Recent state-of-the-art single stage model, which heuristically fuses local and global features, achieves promising trade-off between efficiency and effectiveness. However, we notice that efficiency of existing solutions is still restricted because of their multi-scale inference paradigm. In this paper, we follow the single stage art and obtain further complexity-effectiveness balance by successfully getting rid of multi-scale testing. To achieve this goal, we abandon the widely-used convolution network giving its limitation in exploring diverse visual patterns, and resort to fully attention based framework for robust representation learning motivated by the success of Transformer. Besides applying Transformer for global feature extraction, we devise a local branch composed of window-based multi-head attention and spatial attention to fully exploit local image patterns. Furthermore, we propose to combine the hierarchical local and global features via a cross-attention module, instead of using heuristically fusion as previous art does. With our Deep Attentive Local and Global modeling framework (DALG), extensive experimental results show that efficiency can be significantly improved while maintaining competitive results with the state of the arts.
Library Management in Chuvashia in the 1920s

The article is devoted to the peculiarities of the formation and development of library management of the Chuvash Autonomous Region. The purpose of the article is to consider the state of library management in Chuvashia in the 20s of the twentieth century and the main activities of reading huts and libraries in raising the educational and cultural level of the population in the period under review. The article is written on the basis of a thorough analysis of a wide range of literature on the topic, statistical collections, the study and selection of materials from central and local state archives, most of which are introduced into scientific circulation for the first time. It was revealed that in the years under study, many methods of mass work were developed and tested in practice, which, being improved at subsequent stages in connection with new tasks, have been used and used by libraries to this day: thematic book exhibitions, review evenings, literary evenings, evenings for discussion of new works of fiction, long-term mass campaigns to promote relevant books and libraries among the population, etc. It is concluded that libraries and reading rooms during the period under study gradually gained experience in cultural and educational work: from the mechanical issuance of books, passive service of readers to the search for methods of active work among the masses. By the mid-1920s, libraries and reading rooms began to switch to more organized and planned work.
DESAIN LAPORAN KEUANGAN BERBASIS MICROSOFT EXCEL PADA TOKO BERKAT

This study aims to design the financial statements of Toko Berkatusing the Microsoft Excel application.The research method was qualitative. Instruments of data collection was down through interview in the form of questions regarding the recording of financial statements.While the data collection instrument with documentation, was done through namely evidence of transactions that occurred in the business. The system for recording financial reports at Toko Berkat is still done manually without using an application, so the accounting recording process is not in accordance with SAK EMKM. The financial reports at Toko Berkat are designed using Microsoft Excel. This research can overcome the problem of recording financial statements at Toko Berkat, although in practice the owner is still assisted by researchers to process financial reports using the system that has been created.
CoCoS: Enhancing Semi-supervised Learning on Graphs with Unlabeled Data via Contrastive Context Sharing

Graph Neural Networks (GNNs) have recently become a popular framework for semi-supervised learning on graph-structured data. However, typical GNN models heavily rely on labeled data in the learning process, while ignoring or paying little attention to the data that are unlabeled but available. To make full use of available data, we propose a generic framework, Contrastive Context Sharing (CoCoS), to enhance the learning capacity of GNNs for semi-supervised tasks. By sharing the contextual information among nodes estimated to be in the same class, different nodes can be correlated even if they are unlabeled and remote from each other in the graph. Models can therefore learn different combinations of contextual patterns, which improves the robustness of node representations. Additionally, motivated by recent advances in self-supervised learning, we augment the context sharing strategy by integrating with contrastive learning, which naturally correlates intra-class and inter-class data. Such operations utilize all available data for training and effectively improve a model's learning capacity. CoCoS can be easily extended to a wide range of GNN-based models with little computational overheads. Extensive experiments show that CoCoS considerably enhances typical GNN models, especially when labeled data are sparse in a graph, and achieves state-of-the-art or competitive results in real-world public datasets. The code of CoCoS is available online.
The Ways to Capitalization of the Psychological Resources of the Personality

The aim of the article is to determine peculiarities of capitalization of psy-chological resources in the way of psychological and personal resources.research methods. In the empirical research the methods of psychologi-cal poll, as well as mathematical and statistical methods of discriminating and comparative analysis, method of “causes and effects” are used. The empirical research is implemented by the Goldmark model, which provides for the estab-lishment of alternative hypotheses, allowing to define valuable orientations of the research. the results of the research. It was found that the resources are capital-ized in the way of psychological resourcefulness, such as: resources-“strengths of character” – involvement in a common cause and leadership; interpretive psy-chological resources – love, creativity, kindness to people; psychological survival resources – cognition and reasoning, as well as physical activity; motivational resource of psychological well-being – autonomy; existential resource – free-dom. In the way of personal resourcefulness, such resources are capitalized as: the resource of relationships – psychosocial values; interpretive psychological resources – love and self-improvement; resources-“strengths of character” – meaningfulness, interest in life, gratitude, honesty, sensitivity. conclusions. A person can use only capitalized resources because he/she knows he/she has got them and he/she masters them skillfully. Capitalization of resources in the way of personal resourcefulness, as through appropriation, well-being, values, character traits enables a person to ascertain himself, and characterizes the degree of his achieved significance, respectability. Capitaliza-tion of resources in the way of psychological resourcefulness, as through un-derstanding, opens up the possibilities of self-interpretation, self-change, and shows the extent of its realized possibilities, personal feasibility. We believe that the capitalization of resources in the way of personal resourcefulness enables an individual to achieve effective self-realization in the form of self-made, a way of psychological resourcefulness – unique self-realization of one’s own real life.
Automatic Slides Generation for Scholarly Papers: A Fine-Grained Dataset and Baselines (Student Abstract)

Slides are broadly used to present the research works and there are several studies on the problem of automatic slides generation. However, the lack of dataset hinders further research. In this paper, we construct a benchmark dataset for the problem of slides generation from scholarly papers. The dataset is fine-grained and consists of aligned pairs of single slide and specific region of a paper. Then we deploy several baseline models and conduct preliminary experiments. The results show that this task is challenging and awaits more exploration. The dataset and code will be released.
Clustering Villages Based on Distance and Accessibility to Health Facilities Using the K-Means Method

There are 47 very underdeveloped and 63 underdeveloped villages in Melawi regency. More than 50% of the villages have no health facilities, and the percentage of road lengths with good condition is only 20.53% in Melawi County. One of the most important factors influencing health problems is the physical aspect such as the availability of health facilities. In addition, the distance and easy access to health facilities also influence how quickly people are treated and vaccinated during the Covid 19 pandemic. The objective of this study is to determine the degree of accessibility of health facilities in villages by forming village clusters that are likely to be important to the government in ensuring treatment and distribution of Covid 19 vaccine. The clustering method used is the K-Means method with Euclidean spacing to calculate the spacing of the data and the Elbow method to determine the optimal number of clusters on the data, and the Silhouette coefficient evaluation method to test the degree of accuracy of the model created with K-Means. The results of the Elbow method showed the optimal number of clusters to be 2 clusters. Based on the results of the K-Means algorithm process, the clusters that have a larger average distance and access is rated as difficult are cluster 1 with 92 villages in it, and cluster 1 has a smaller average distance and access is relatively easy with 77 villages in it. The result of the evaluation with the silhouette coefficient is 0.299.
A SCOPE REVIEW ON MOBILE LEARNING ADOPTION IN EDUCATIONAL INSTITUTION IN MALAYSIA DURING THE PANDEMIC COVID-19

This article aims to survey the scope of mobile learning in educational institutions in Malaysia during the Covid-19 pandemic. M-learning is a suitable alternative method used during the Covid-19 pandemic for students to learn at home. The review focuses on implementing remote teaching for engineering laboratory courses during pandemic Covid-19—article journal selected from the Science Direct and Web of Science (WoS) database. The articles were evaluated, reviewed, and analysed through eligibility process. Technology elements can make students more fun in learning, motivated and skilled through mobile learning. This study can prepare students to apply the device in a broader learning process to acquire new knowledge and skills regardless of time and place. This paper recommends that further studies look at how learning material quality factors can influence students' decision to accept m-learning in the Covid-19 pandemic crisis.
Light field spatial super‐resolution via geometric feature interaction

Light field (LF) enables high-dimensional image data representation since it can capture spatial and angular information of light rays simultaneously. The low spatial resolution caused by the limited imaging ability of the capturing equipment and the trade-off between spatial and angular resolution greatly affects the quality and application of LF images. Here, an end-to-end LF super-resolution method is proposed via geometric feature interaction. First, the low-resolution LF images are stacked in the horizontal and vertical epipolar plane image (EPI) directions and form 3D VI stacks. Then, these stacks are put into a dual-branch network, and 3D convolution is alternately performed on the viewpoint images (VIs) and EPIs by reshaping features for better feature extraction and interaction. The proposed method can fully explore the texture information and geometric consistency of the LF, and super-resolve all VIs at the same time. Experimental results on both real-world and synthetic LF datasets show that the proposed method has higher performance than other state-of-the-art methods.