Reviewerly/exHarmony huggingface dataset:
Rice Planting Calendar Application Development using Scrum

Indonesia is an agricultural country that produces more rice commodities than secondary crops. Many people who work as farmers choose the land to plant rice. Farmers experience several obstacles in determining the correct planting time to improve the rice harvest quality. A planting calendar is a method used by farmers to determine the scheduling of planting for one year. The rice planting calendar works based on rainfall and climate patterns. With the help of the latest technology, determining the rice planting calendar can be done quickly. The utilization of computer technology and algorithms such as Artificial Neural Network is helpful for forecasting rainfall using time series data accurately in the following month. The planting calendar is connected to data from the Meteorology, Climatology and Geophysics Agency (BMKG) from each station in each region. The rice planting calendar is made on a mobile basis with the aim of providing convenience for users in their hands. This cropping calendar application was developed using the Scrum method. The application development stages consist of sprint planning, first sprint, second sprint, third sprint and usability testing. The results of the development of the sprint went well. After completing the story, it was continued with the usability testing stage using the System Usability Scale (SUS). The SUS test was given to 20 respondents who had criteria including farmers and landowners. The results of SUS on the rice planting calendar application got a score of 72.75, which was categorized as Good.
Study on OBE Teaching Concept in the Context of Deep Learning for the Construction of University Mathematics Microcourses

Outcome-Based Education (OBE) is a goal-based educational system in which each part of education is around outcomes. By the end of the course, every student should have achieved the goal. Outcome-Based Education (OBE) involves various teaching methods and is not restricted to any specified way of teaching. Based on the targeted results, the teacher will mentor the students by acting as an instructor, trainer, and facilitator. The Deep Learning Technology of Artificial Intelligence is applied in various applications to carry out automation and physical tasks without human intervention along with data transfer through wireless networking. In this research, an Apriori Algorithm supports the identification of a suitable method for the teaching process (the OBE Teaching concept) through the outcome of the learning process. This optimization of identification of suitable method is performed with the implementation of Ant Colony Optimization (ACO) in the construction of the University Mathematics Course. The study results proved that the proposed algorithm provides an accuracy of 98.87%. The proposed algorithm can be trained further based on different rules to attain some increased performance of the methodology.
Brainstorm on artificial intelligence applications and evaluation of their commercial impact

&lt;div class="page" title="Page 1"&gt;&lt;div class="layoutArea"&gt;&lt;div class="column"&gt;&lt;p&gt;&lt;span lang="EN-US"&gt;A countless number of artificial intelligence applications exist in a wide range of fields. The artificial intelligence (AI) technology is becoming mature, free powerful libraries enable programmers to generate new apps using a few lines of code. The study identifies the applications that are the most interesting for a developer as far as profit is concerned. Some AI applications related to trading, industry, sales, logistics, games, and personal services have been considered. To select the most promising AI applications, multi criteria methods have been adopted. This brainstorm may be useful to inspire new born start-ups, willing to create viral apps/products. The paper wishes to be informative and light, for further information, a rich selection of publications and books is provided.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;
The King Is Naked: On the Notion of Robustness for Natural Language Processing

There is growing evidence that the classical notion of adversarial robustness originally introduced for images has been adopted as a de facto standard by a large part of the NLP research community. We show that this notion is problematic in the context of NLP as it considers a narrow spectrum of linguistic phenomena. In this paper, we argue for semantic robustness, which is better aligned with the human concept of linguistic fidelity. We characterize semantic robustness in terms of biases that it is expected to induce in a model. We study semantic robustness of a range of vanilla and robustly trained architectures using a template-based generative test bed. We complement the analysis with empirical evidence that, despite being harder to implement, semantic robustness can improve performance %gives guarantees for on complex linguistic phenomena where models robust in the classical sense fail.
Siamese BERT-Based Model for Web Search Relevance Ranking Evaluated on a New Czech Dataset

Web search engines focus on serving highly relevant results within hundreds of milliseconds. Pre-trained language transformer models such as BERT are therefore hard to use in this scenario due to their high computational demands. We present our real-time approach to the document ranking problem leveraging a BERT-based siamese architecture. The model is already deployed in a commercial search engine and it improves production performance by more than 3%. For further research and evaluation, we release DaReCzech, a unique data set of 1.6 million Czech user query-document pairs with manually assigned relevance levels. We also release Small-E-Czech, an Electra-small language model pre-trained on a large Czech corpus. We believe this data will support endeavours both of search relevance and multilingual-focused research communities.
Geochemical exploration in Mesillas geothermal area, Mexico

This work presents results of geochemical exploration in the promising geothermal area of Mesillas, Nayarit, in the western border of the Mexican Volcanic Belt. The assessment of the geothermal resource was based on a geochemical approach which consisted in: (i) mapping and geological survey, (ii) chemical characterization of geothermal manifestations, (iii) diffuse flux analysis (CO2 and H2S), (iv) temperature at depth calculations using both classical and integrated multicomponent geothermometry.. Additional to the technical work, social dissemination of geochemical issues was attempted by designing and distributing a geomap among local people. The geological survey identified five andesitic units in an area of ∼1.5 km2, where 25 thermal springs discharge Na–CO3 water with temperatures between 47 and 90 °C, and pH ∼9. Geothermometric calculations indicate a bottom temperature between 125 °C and 130 °C. Andesites show high permeability generated by intense fracturing related with normal fault systems with trend NW-SE, SW-NE, NS, and EW. Surface hydrothermal alteration is mainly argillic with silicification. CO2 and H2S hydrothermal degassing are mainly restricted to thermal springs aligned in the NW-SE direction. Thermal springs with high temperature, highest degassing and high presence of hydrothermal alteration occur in the south-southwest zone of the geothermal area (MOL10, MOL13, MOL14 and MOL15); they correspond with NW-SE structural alignment. Furthermore, the estimated range of temperature at depth using geothermometers is 125–130 °C, and the surface temperature is on average 20 °C; considering local thermal gradient, it was estimated an equilibrium depth of 2.0–2.1 km. These results indicate that the Mesillas geothermal prospect may be classified as a medium temperature system with good permeability that may be exploited for electricity production as well as direct uses.
LUNA: Localizing Unfamiliarity Near Acquaintance for Open-Set Long-Tailed Recognition

The predefined artificially-balanced training classes in object recognition have limited capability in modeling real-world scenarios where objects are imbalanced-distributed with unknown classes. In this paper, we discuss a promising solution to the Open-set Long-Tailed Recognition (OLTR) task utilizing metric learning. Firstly, we propose a distribution-sensitive loss, which weighs more on the tail classes to decrease the intra-class distance in the feature space. Building upon these concentrated feature clusters, a local-density-based metric is introduced, called Localizing Unfamiliarity Near Acquaintance (LUNA), to measure the novelty of a testing sample. LUNA is flexible with different cluster sizes and is reliable on the cluster boundary by considering neighbors of different properties. Moreover, contrary to most of the existing works that alleviate the open-set detection as a simple binary decision, LUNA is a quantitative measurement with interpretable meanings. Our proposed method exceeds the state-of-the-art algorithm by 4-6% in the closed-set recognition accuracy and 4% in F-measure under the open-set on the public benchmark datasets, including our own newly introduced fine-grained OLTR dataset about marine species (MS-LT), which is the first naturally-distributed OLTR dataset revealing the genuine genetic relationships of the classes.
Boost Supervised Pretraining for Visual Transfer Learning: Implications of Self-Supervised Contrastive Representation Learning

Unsupervised pretraining based on contrastive learning has made significant progress recently and showed comparable or even superior transfer learning performance to traditional supervised pretraining on various tasks. In this work, we first empirically investigate when and why unsupervised pretraining surpasses supervised counterparts for image classification tasks with a series of control experiments. Besides the commonly used accuracy, we further analyze the results qualitatively with the class activation maps and assess the learned representations quantitatively with the representation entropy and uniformity. Our core finding is that it is the amount of information effectively perceived by the learning model that is crucial to transfer learning, instead of absolute size of the dataset. Based on this finding, we propose Classification Activation Map guided contrastive (CAMtrast) learning which better utilizes the label supervsion to strengthen supervised pretraining, by making the networks perceive more information from the training images. CAMtrast is evaluated with three fundamental visual learning tasks: image recognition, object detection, and semantic segmentation, on various public datasets. Experimental results show that our CAMtrast effectively improves the performance of supervised pretraining, and that its performance is superior to both unsupervised counterparts and a recent related work which similarly attempted improving supervised pretraining.
Manipulation at optimum locations for maximum force transmission with mobile robots under environmental disturbances

Abstract Remote manipulation plays a key role for applications in hazardous conditions, yet designing a robust controller enabling safe interaction with unknown environment and under the influence of disturbances is a challenge. In this study, we propose effective control and optimization methods for mobile robotic manipulator systems that can increase effort transmission to a task in desired directions. The vehicle position is optimized by utilizing constrained particle swarm optimization where the objective is to enhance directional manipulability of the robotic arm within the system. A forward dynamic controller is implemented to eliminate undesired excessive motions near singular joint configurations. A reset control algorithm along with an admittance type controller are developed for stable interaction with an unknown object under environmental disturbances. The experimentally validated results show that the proposed method phase out undesired position disturbances and increase the directional manipulability for the required task enabling augmented effort transmission for the task execution.
SimCTC: A Simple Contrast Learning Method of Text Clustering (Student Abstract)

This paper presents SimCTC, a simple contrastive learning (CL) framework that greatly advances the state-of-the-art text clustering models. In SimCTC, a pre-trained BERT model first maps the input sequence to the representation space, which is then followed by three different loss function heads: Clustering head, Instance-CL head and Cluster-CL head. Experimental results on multiple benchmark datasets demonstrate that SimCTC remarkably outperforms 6 competitive text clustering methods with 1%-6% improvement on Accuracy (ACC) and 1%-4% improvement on Normalized Mutual Information (NMI). Moreover, our results also show that the clustering performance can be further improved by setting an appropriate number of clusters in the cluster-level objective.
LiDAR–camera fusion for road detection using a recurrent conditional random field model

Abstract Reliable road detection is an essential task in autonomous driving systems. Two categories of sensors are commonly used, cameras and light detection and ranging (LiDAR), each of which can provide corresponding supplements. Nevertheless, existing sensor fusion methods do not fully utilize multimodal data. Most of them are dominated by images and take point clouds as a supplement rather than making the best of them, and the correlation between modalities is ignored. This paper proposes a recurrent conditional random field (R-CRF) model to fuse images and point clouds for road detection. The R-CRF model integrates results (information) from modalities in a probabilistic way. Each modality is independently processed with its semantic segmentation network. The probability scores obtained are considered a unary term for individual pixel nodes in a random field, while RGB images and the densified LiDAR images are used as pairwise terms. The energy function is then iteratively optimized by mean-field variational inference, and the labelling results are refined by exploiting fully connected graphs of the RGB image and LiDAR images. Extensive experiments are conducted on the public KITTI-Road dataset, and the proposed method achieves competitive performance.
Contextual Mathematical Modelling: Problem-Solving Characterization and Feasibility

The current study investigates contextual mathematical modelling (MM) problems that were retrieved from authentic workplace situations and simplified for formal secondary school math lessons. First, the study aims to characterize contextual MM problems according to Schoenfeld’s framework of problem-solving (PS). Second, it aims to investigate the perceptions of two stakeholder groups: (1) math experts and policymakers and (2) math teachers with respect to the characteristics of the contextual MM problems and their feasibility regarding implementation in secondary school education. Based on the Delphi methodology, we employed two phases for our analysis: an open-ended questionnaire to interview ten stakeholders and, subsequently, a Likert-type questionnaire to collect data from 122 stakeholders. The main results indicate that the contextual MM problems are characterized by PS. A similar view was expressed by different stakeholder groups, and no differences were caused by various background variables, such as educational level or STEM background. Additionally, the findings revealed that both stakeholder groups perceived that it is highly feasible for these problems to be integrated into secondary school education. This study contributes theoretically to the interrelationship between MM and PS frameworks, and provides practical recommendations for the implementation of contextual MM problems in secondary schools by applying PS skills.
Transformer-Based GAN for New Hairstyle Generative Networks

Traditional GAN-based image generation networks cannot accurately and naturally fuse surrounding features in local image generation tasks, especially in hairstyle generation tasks. To this end, we propose a novel transformer-based GAN for new hairstyle generation networks. The network framework comprises two modules: Face segmentation (F) and Transformer Generative Hairstyle (TGH) modules. The F module is used for the detection of facial and hairstyle features and the extraction of global feature masks and facial feature maps. In the TGH module, we design a transformer-based GAN to generate hairstyles and fix the details of the fusion part of faces and hairstyles in the new hairstyle generation process. To verify the effectiveness of our model, CelebA-HQ (Large-scale CelebFaces Attribute) and FFHQ (Flickr-Faces-HQ) are adopted to train and test our proposed model. In the image evaluation test used, FID, PSNR, and SSIM image evaluation methods are used to test our model and compare it with other excellent image generation networks. Our proposed model is more robust in terms of test scores and real image generation.
The Scope of Crime by Social Origin in the Region of Gjilan

This paper examines the findings of a research conducted in the Gjilan area over the previous five years on the extent of crime by socioeconomic origin (mostly offenders of criminal acts in six towns) (ALFA, BETA, GAMMA, DELTA, and EPSILON). The location of the examination in the Gjilan area is given additional emphasis mostly in methodological portion. We incorporated words that more accurately characterize the studied issue and its interpretative position in the observed occurrence in the study and discussion outcomes. This study was used as a statistical method as a very favorable method (review using tables and graphs) that is developed in space and certain periods of data gathering, review, and presentation related to the extent of crime, according to the social origin of crime, and according to the dynamic rate of spread in the countries with the largest populations. The data was gathered using empirical methods. Empirical data was acquired from security source records in relation with cases classified as criminal crimes. The findings revealed statistically significant disparities in the extent of crime over the research period between people committing criminal acts based on their socioeconomic background (rural and urban areas). There are additional disparities in the fact that crime occurs more dynamically and to a greater extent in larger towns. The study will add to and complete the body of scientific information on the issue of criminal populations. The study's findings will aid in the development of new strategies and approaches for reducing the criminal population in the Gjilan region and preventing particular types of crime. Research is also significant for social practice and practical reasons such as controlling and properly preventing crime.&#x0D; &#x0D; &#x0D; Received: 5 March 2022 / Accepted: 26 June 2022 / Published: 5 July 2022
Quantitative Courses in Higher Education: A Comparison Between Asynchronous and Synchronous Distance Learning

Quantitative courses in higher education have always been difficult and demanding because they involve complex principles and procedures and students are required to deal with complicated problems. With the outbreak of the COVID-19 crisis and the need to move immediately to distance learning, the challenge involved in undertaking such courses has become even more significant.&#x0D; &#x0D; Recent studies point to different findings regarding the effect of asynchronous and synchronous learning on student performance so it is not entirely clear which method is preferable. The present study addresses the above ambiguity with a focus on quantitative courses, known for their special complexity. The study examined which method is preferable and why, based on the learners&amp;rsquo; point of view. Two main issues were examined: the contribution of each method to the learning process and whether one of them is significantly superior to the other and can even replace it exclusively. &#x0D; &#x0D; The research was based on two samples of students &amp;nbsp;who enrolled in eight quantitative courses at two colleges. The courses were given during the year 2020-2021 using combined teaching formats: Asynchronous and synchronous distance learning. Learners were asked to answer an online questionnaire that assessed the characteristics and advantages/disadvantages of both methods. &#x0D; &#x0D; The findings show that students distinctly prefer asynchronous learning over synchronous. The main reasons for this are the ability to repeat lessons indefinitely, time savings, flexible learning anywhere, anytime and at the appropriate pace as well as the ability to better understand the material.
A heterogeneous couplings and persuasive user/item information model for Next Basket recommendation

Next Basket Recommender Systems (NBRS) are session-based recommenders that leverage a user’s sequential behaviour and context to predict the next items they could buy during the next shop visit. This field is popular now. Current research works rely primarily on item–user relations, ignoring complicated heterogeneous couplings and persuasive item/user information, causing sparsity and cold-start, stifling NBRS performance. This research implements a Heterogeneous Coupling and Persuasive User/Item Information Model (HCPIM) comprising of heterogeneous couplings and a persuasive embedding layer (HCPEL), the consecutive basket layer, and a basket prediction layer. The HCPEL learns heterogeneous couplings (user–user, user–item, item–item) and persuasive user information for every basket to model the composite intra-basket associations across the items inside a basket given a user’s behaviour sequences. The consecutive basket layer learns inter-basket associations from the HCPEL using a Gated Recurrent Unit network. In the basket prediction layer, an improved Particle Swarm Optimization (PSO) algorithm optimizes the network’s weight and bias to iteratively learn heterogeneous couplings and persuasive information inside and across baskets to recommend the next basket. In PSO, we incorporate the exploratory Gravitation Search Algorithm to balance exploration and exploitation. Our architecture includes an Adaptive Response to Particle Adjustment Strategy to increase exploitation and prevent particles from being trapped. Extensive tests are run to evaluate HCPIM on TaFeng and the IJCAI 2015 dataset. The HCPIM improves performance by 27.87, 29.5, and 27.29 percent when density is 50, 100, and 150 on the IJCAI-15 dataset, and 8.53, 10.57, and 4.85 percent on the TaFeng dataset.
Knowledge Management in Relationship Among Abusive Management, Self-Efficacy, and Corporate Performance Under Artificial Intelligence

The purpose is to explore the application potential of HCI (Human-Computer Interaction) technology under AI (Artificial Intelligence) in enterprise performance evaluation and the influence of abusive management and self-efficacy on enterprise performance. Guided by psychological theory, employees from a listed real estate enterprise are selected, and the research themes of abusive management, self-efficacy, and employee performance are assumed. Afterward, the employee job satisfaction and performance evaluation model and system interface based on deep learning BPNN (BackPropagation Neural Network), SVM (Support Vector Machine) regression, and HCI are innovatively proposed. The results show that the HCI interface can be accessed accurately according to the employee's verbal instructions. BPNN model has reached the best performance at the iteration of 70times, and all indexes have reached the expected employee satisfaction.