Reviewerly/exHarmony huggingface dataset:
COMPONENTS OF SOCIAL RESPONSIBILITY PERSONALITIES: SOCIAL AND PSYCHOLOGICAL CONTENT

The article reveals the socio-psychological content of the components of social responsibility of an individual. It is shown that the social responsibility of an individual is defined as a moral and value stable personal formation, which includes a strategy of social behavior of an individual, where the basis is a conscious attitude to voluntary, active performance of social roles, in accordance with one's actions and their consequences, values and norms of society, as well as ready to be responsible for the obtained result. It is emphasized that social responsibility is revealed in the following structural components: value-normative (sociocentric motivation, egocentric motivation, etc.), cognitive-informational (cognitive consciousness, awareness, etc.), affective-empathic (sthenic emotionality, asthenic emotionality, sincerity and etc.), organizational-communicative (dynamic ergicity, dynamic aergy, difficulties, etc.), result-active (object effectiveness, subject effectiveness, etc.), reflexive-prognostic (regulatory internality, regulatory externality, etc.). The conducted study of the components of social responsibility revealed the specificity of their interrelationships depending on the length of professional activity, as well as evidenced an increase in the level of responsibility to the level of hyperresponsibility. Professionally and socially responsible respondents are aware of themselves as a subject of activity, show social activity, initiative, control their educational and professional activities.
Visualization of Volcanic Landform with Augmented Reality

Visualization of volcanic landforms with digital elevation model (DEM) data on the GIS desktop computer has limitations regarding access to the software display function and its use outside the room. This obstacle can be overcome by combining GIS and mobile augmented Reality. However, using digital elevation model data and satellite imagery in augmented reality software has not been widely explored. This study shows a practical way of integrating geospatial data with mobile augmented reality software. This research aims to create an augmented reality prototype to visualize virtual volcano objects. DEM data and satellite imagery create 3-dimensional (3D) objects and provide texture characteristics of volcanic landform objects. Visualization of volcanic landforms is done through an augmented reality (AR) application prototype on an Android smartphone. The experimental results show that augmented reality technology effortlessly combines DEM data and land features from satellite imagery. Evaluation of a smartphone device's manufacturing and installation stages shows results with a "successful" status. Through follow-up testing nine times, the AR prototype showed positive performance in all aspects of distance, angle, and lighting. The final test scenario in the form of responses regarding the application from other users obtained an average value of the "Good" category of 92.8%. In conclusion, these results indicate that the system built can be used in learning. However, further research requires more diverse test scenarios to see how far this application prototype can be used in various conditions. Keywords: Digital Elevation Model, GIS, Augmented Realit, 3D Visualization
The Effect of The Prediction of The K-Nearest Neighbor Algorithm on Surviving COVID-19 Patients in Indonesia

This study aims to measure the prediction of survival of covid-19 patients with the best algorithm based on RMSE(Root Mean Square Error). The Covid-19 pandemic has lasted from December 2019 until now and is full of uncertainty about when this pandemic will end, so this research was carried out. In this study, the knowledge discovery database method was used by extracting data sets from Covid-19 patients from March 2020 to March 2021 for each province in Indonesia (Dataset from Kawal Covid-19 SintaRistekbrin) to predict survival during this pandemic as measured by the best algorithms include k-NN (k-Nearest Neighbor), SVM (Support Vector Machine), and/or Deep Learning. The measurement results using cross-validation and the optimal number of folds is 3 in the form of RSME, showing that the k-NN algorithm is an algorithm with RSME 0.101 +/-0.23 where the error rate is the lowest compared to the two algorithms above. Therefore, the k-NN algorithm was chosen as the algorithm for the predictive measurement of surviving Covid-19 patients.
Design and Build of IoT Based Flood Prone Monitoring System at Semani’s Pump House Drainage System

Floods are a common disaster in watersheds, and flood control is difficult. However, losses can be reduced by quickly disseminating alert status information. This paper proposes a prototype of a monitoring system that can determine the status of flood alerts in real time and quickly disseminating to the community, allowing people to be better prepared for flood disasters. The system was developed using the RD method and consists of hardware and software development. The hardware comprises several sensor modules to read the discharge, temperature, humidity, and water level and to transmit the readings to the software. The software is divided into two applications: a website application and a Telegram application. The public can find the flood alert status history data from the website and obtain flood alert status warning messages and the latest alert status from Telegram. The results of the tests indicated that the sensors were very accurate, with a MAPE value of less than 10%. The software test also showed that the input and output were according to design. The proposed system can potentially reduce flood losses by providing early warning information to the community. The system is also scalable and adaptable to other watersheds.
Combination of the MADM Model Yager and k-NN to Group Single Tuition Payments

Tuition payments at State Universities (PTN) use a Single Tuition Fee (UKT) payment system. It has been implemented to make it easier for students to pay their tuition. The UKT system is divided into several groups starting from the UKT group I to VIII. Universitas Sembilanbelas November (USN) Kolaka is a state university and the university should determine the amount of tuition fees for each student according to the UKT system. In determining the UKT group for each student, several variables were used to make it easier to group student into their UKT groups. However, the large number of students, a number of variables and the limited time to determine the amount of UKT for each student become an issue, so a method was needed to help USN Kolaka in grouping UKT for each student. One thing that can be done was to use the MADM model Yager and k-NN in order to make it easier to group UKT students. The results of the study showed that the use of the MADM Model Yager and k-NN could determine the UKT group of the students, and the results obtained for the UKT group I were 63 people (21.95%), the UKT group II were 72 people (25.09%), the UKT group III were 120 people (41.81%), UKT group IV were 7 people (2.44%), and UKT group V were 25 people (8.71%).
Emirati University Students’ Use of the Request Speech Act in Arabic in Email Communication with Faculty

Students’ academic performance is greatly affected by their communication skills, a significant one of which is email writing. Due to technological advances and the distance education imposed by the COVID-19 pandemic, a great deal of communication between students and professors is in the form of email communication. Students’ inattention to the rules of this particular genre may result in face-threatening situations. Prompted by the lack of research on university students’ utilization of the request speech act in Arabic emails, this study investigated 96 Emirati first-year students’ request behaviors in Arabic in communication with university professors. Data were collected using a discourse-completion task and an evaluation form filled in by two judges. Results showed that the students mostly produced a direct request speech act. However, a significant number of them failed to follow the expected discourse structure often omitting the subject line, not thanking or writing their names at the end. The judges thought the emails were generally respectful. However, they felt they were not appropriate, which was mainly caused by the frequent mistakes with spelling, punctuation, and vocabulary. There were also instances of informal language use and grammatical errors. Based on these results, recommendations are provided focusing on awareness-raising and practice activities.
The influence of the use of terrarium media on Bryophyta sub-material on the learning outcomes of class X students of SMA Negeri 3 Wajo

One of the media that can be used in helping the learning process on Bryophyta material is the Terrarium. This study aims to (1) Describe the learning outcomes of students on the Bryophyta sub material for class X SMA Negeri 3 Wajo which is taught without using terrarium media; (2) Describe the learning outcomes of students on the Bryophyta sub material for class X SMA Negeri 3 Wajo taught using terrarium media; and (3) Knowing the learning outcomes of class X SMA Negeri 3 Wajo on sub Bryophyta material taught using terrarium media and not. The type of research used was an experimental quasi with a nonequivalent control group design. The data-collection techniques used are tests of pretest and posttest. The study conducted descriptive and inferential statistical tests (1) normality tests, (2) homogeneity tests, and (3) hypothetical tests. The results showed that there was an effect of using terrarium media on the Bryophyta sub-material on the learning outcomes of class X students at SMA Negeri 3 Wajo. This is proved after testing the hypothesis. The hypothesis in this study can be said to be proven because 0.000 &lt; 0.05 or by looking at tcount of 4.614 while ttable of 2.006 thus tcount&gt; ttable. This means that there is a significant difference between the learning outcomes of students who are taught using terrarium media and not. This shows that there is an effect of using terrarium media on the learning outcomes of class X students of SMA Negeri 3 Wajo. The implications of this research are (1) For students, it is easy to understand the material by looking directly at objects related to the material so that it helps students get maximum results; (2) For educators, it can be a reference for variations of learning media that can be used in further learning; and (3) For researchers, it can add insight into the variety of learning media that can be used in the learning process.
Construction of Cloud Computing and Big Data Course Teaching System Based on CDIO

CDIO education model has become a hotspot in the field of education with its emphasis on the cultivation of engineering practice and design thinking ability, while cloud computing and big data, as an important direction of the current development of information technology, have become increasingly important in higher education. This paper mainly elaborates on the definition of CDIO education model, advantageous features, the relationship and mutual influence between cloud computing and big data. After that, it puts forward specific measures for the construction of the course teaching system, including the principles of course design, course content and teaching methods, evaluation and improvement of DIO. The in-depth discussion and practice of this study will provide useful reference and guidance for the teaching of cloud computing and big data courses.
Back to the basics: : Applying multilingual dictionary analysis to the Comparative Manifesto Project corpus

For researchers interested in political communication and electoral politics, the Comparative Manifesto Project (CMP) is a widely-used database, which contains an extensive repository of annotated electoral manifestos. However, by relying on the fact that country-specific expert coders assign text excerpts to different policy areas, researchers frequently fail to engage with the multilingual nature of the corpus. This article uses a deductively-defined multilingual dictionary analysis to extract information on a set of electorally salient policies in seven European countries. In this application, I not only show that inter-coder reliability tests are encouragingly high and can be conducted relatively rapidly, but I also illustrate that the usage of multilingual policy-specific dictionary analysis is linked with high precision and high recall metrics. This approach can not only be helpful in examining electoral issue salience, but can also help in uncovering the language and the framing that political parties employ when discussing different policies.
Research on the Path of Accelerating the Cultivation of Foreign-related Legal Talents in the New Era

Foreign-related rule of law talents are responsible for incorporating national governance capability and governance system into foreign relations, striving to construct excellent laws and regulations that adapt to the foreign environment and shape a sound foreign governance model. However, in the current world undergoing unprecedented changes, there is a shortage of high-quality foreign-related rule of law talents in China. The mechanism for cultivating foreign-related talents is not sound, and there is a gap between theory and practice. It is necessary to improve the mechanism for cultivating foreign-related rule of law talents, optimize talent development plans under the new model of humanities and social sciences, and coordinate the integration of theory and practice. This will provide solid support for the development of national governance capabilities and the rule of law in foreign affairs in the new era.
Evaluating Transferability in Multilingual Text Analyses

Multilingual text analysis is increasingly important to address the current narrow focus of English and other Indo-European languages in comparative studies. However, there has been a lack of a comprehensive approach to evaluate the validity of multilingual text analytic methods across different language contexts. To address this issue, we propose that the validity of multilingual text analysis should be studied through the lens of transferability, which assesses the extent to which the performance of a multilingual text analytic method can be maintained when switching from one language context to another. We first formally conceptualize transferability in multilingual text analysis as a measure of whether the method is equivalent across language groups (linguistic transferability) and societal contexts (contextual transferability). We propose a model-agnostic approach to evaluate transferability using (1) natural and synthetic data pairs, (2) manual annotation of errors, and (3) the Local Interpretable Model-Agnostic Explanations (LIME) technique. As an application of our approach, we analyze the transferability of a multilingual BERT (mBERT) model fine-tuned with annotated manifestos and media texts from five Indo-European language-speaking countries of the Comparative Agendas Project. The transferability is then evaluated using natural and synthetic parliamentary data from the UK, Basque, Hong Kong, and Taiwan. Through the evaluation of transferability, this study sheds light on the common causes that lead to prediction errors in multilingual text classification using mBERT.
Efficient Vision Transformer for Human-Centric AIoT Applications Through Token Tracking Assignment

The integration of Artificial Intelligence Internet of Things (AIoT) with consumer electronics has resulted in enhanced connectivity and intelligence within the consumer electronics sector, thereby facilitating a more convenient living experience for individuals. Particularly, the utilization of Vision Transformer (ViT) in AIoT has further improved the decision-making capabilities of consumer electronics devices. To cater to the increasing demand for efficient and intelligent consumer electronics, the prevalent approach involves transferring ViT models, which are trained on AIoT cloud servers, to edge devices for deployment. However, the incongruity between resource-constrained edge devices and computationally intensive ViT models necessitates the reduction of redundant computations in ViT to optimize the utilization of edge devices. This paper introduces a novel approach named TMTP, which combines token merging and pruning techniques. TMTP encompasses three key blocks: the Token Merging Block (TMB), the Token Tracking Assignment Block (TAB), and the Token Pruning Block (TPB). TMTP effectively diminishes redundant computations in the ViT model while preserving its accuracy. The experimental results demonstrate that by employing TMTP to hierarchically prune 80% of the input tokens in the SWAG model, the number of GFLOPs is significantly reduced by 35%. Interestingly, the accuracy of ImageNet-1k experiences a mere reduction of 0.05%-0.18%.
Consistency Center-Based Deep Cross-Modal Hashing for Multisource Remote Sensing Image Retrieval

Cross-modal hashing aims to retrieve similar images from large-scale Earth Observation (EO) data archives, which typically contain multiple satellite sources of remote sensing (RS) images. However, existing cross-modal hashing methods primarily focus on dual-source RS images and often face two main limitations when retrieving multi-source RS images. Firstly, these methods exhibit significant redundancy as they require handling all possible dual-source combinations in multi-source RS images. Secondly, they often rely on pairwise or triplet image sources to construct objective functions, which are not significantly effective in reducing the discrepancies among multiple RS image sources. To address these limitations, we propose a novel Consistency Center-based deep cross-modal Hashing method called C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Hash for multi-source RS image retrieval. Our C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Hash employs a multi-branch hashing network to directly encode multi-source RS images into unified hash codes, thereby offering higher processing efficiency. Furthermore, C <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> Hash introduces consistency centers to construct a novel objective function. The consistency center represents the shared semantic features among similar multi-source RS images and is generated by a label hashing network. The objective function encourages similar multi-source RS images to approach the same consistency center to align all image sources in a unified Hamming space. Our method can effectively reduce the discrepancies across multiple image sources and generate unified hash codes. To evaluate its effectiveness, we construct a new Multi-Source RS Image dataset called MSRSI, comprising five different types of image sources. We conduct comprehensive experiments to demonstrate the superior performance of our method on the MSRSI dataset. https://github.com/sunyuxi/C2Hash.
DySPN: Learning Dynamic Affinity for Image-guided Depth Completion

Image-guided depth completion (IGDC) is a multimodal computer vision task for acquiring high-precision dense depth maps. It reasonably predicts values around accurate sparse depth measurements by relying on the details of simultaneous dense RGB images. To achieve this goal, spatial propagation networks (SPNs) elaborate context-aware meta cells that connect pixels to their neighbors and fuse sparse and dense modalities by linear propagation. However, static affinity matrices and fixed neighborhood connections limit the representation of the networks. In this paper, our proposed dynamic SPN (DySPN) uses a nonlinear propagation model (NLPM), which processes the propagation more finely by adjusting the affinity weights, diffusion paths, and the number of neighbors. Specifically, we first generate adaptive weighting (AW) matrices by decoupling the neighborhood into parts with respect to different distances. Independent attention maps are recursively applied to refine the weight value. Furthermore, a dynamic path (DP) strategy is adopted to unfreeze the links of the neighborhood for learning variable connections. The solution space of the paths is also constrained by a propagation decay loss to keep the results stable. Finally, we introduce a diffusion suppression (DS) operation, which preserves the edge of dense depth maps by manipulating the AW and DP strategies to decelerate and terminate the propagation. In our experiment, the proposed method requires fewer iterations and neighbors than other SPNs while yielding better results. DySPN outperforms state-of-the-art (SoTA) methods on the KITtI DC, NYU Depth v2, and VOID datasets. Our code is available at: https://github.com/Kyakaka/DySPN.
Instance-Dictionary Learning for Open-World Object Detection in Autonomous Driving Scenarios

This paper addresses an important and valuable open-world object detection (OWOD) in autonomous driving scenarios, which aims to detect objects under both <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">domain-agnostic</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">category-agnostic</i> settings simultaneously. Existing OWOD algorithms mainly focus on the detection of pre-defined object categories under various conditions (domain-agnostic) or instead perform zero-shot object detection (category-agnostic), separately. The knowledge gap between seen and unseen object categories poses challenges for models optimized with supervision from the only seen object categories. The domain difference across different scenarios also causes further challenges in aligning observations with different appearances. To address these two challenges simultaneously, we propose our Instance Dictionary Learning (IDL for short) for more robust and accurate OWOD performance. We first design a pre-training procedure to build up the mappings between region features and category semantic embeddings by introducing instance contrastive learning. The joint vision-semantic space is formulated through the more detailed instance-level “Dictionary”, which expresses the region-category correspondences and helps link the seen and unseen object categories. The domain discrimination is further designed for extracting the domain invariance feature representations in the further training procedure seamlessly. The proposed IDL could detect the unseen categories from unseen domains without any bounding box annotations while there is no obvious performance drop on detecting seen categories meanwhile. Comprehensive experiments have been conducted and our method could achieve a new state-of-the-art OWOD performance over previous algorithms.