Reviewerly/exHarmony huggingface dataset:
A Task Processing Efficiency Improvement Scheme Based on Cloud-Edge Architecture in Dense Computing Environments

As cloud-edge architecture combines low latency and high-performance computing, it has a wider range of application scenarios. Considering that, how to reasonably allocate tasks to the edge cloud or the central cloud and how to select appropriate virtual machines (VMs) for tasks are two key aspects affecting the efficiency of cloud-edge architecture. For the former, the ARDT algorithm is proposed, which dynamically adjusts the optimal threshold by predicting the number of real-time edge-end devices. For the latter, we design the DBMR algorithm based on the Max-Fit algorithm, in which we propose a structure of blocking to reduce the time complexity of the original algorithm from O(n2) to O(n(n-m)). Experimental results show that the ARDT-DBMR algorithm optimizes the average processing time and task failure rate by 30%-40% compared with the baseline algorithms and other advanced algorithms and significantly improves the problem of abnormal fluctuations in VM utilization in dense computing environments.
The Impacts of Cash Transfers on Mental Health and Investments: Experimental Evidence from Mali

Stress and cognitive burden associated with poverty constrain decision-making regarding investments in the future, which can in turn perpetuate poverty. We exploit the randomized roll-out of Mali’s national cash transfer program to estimate its impact on measures of psychological and emotional well-being, time preferences, and cognitive function among households’ primary decision-makers. We find that receiving transfers reduced decision-makers’ self-reported stress and worry, as well as improved their self-esteem. The program did not affect measures of cognitive function but led to a modest increase in a measure of patience. Consistent with reduced stress, improved self-esteem, and increased patience, the program also increased investments in productive assets. Results suggest that, in addition to cash transfers providing the economic resources to support investments in the future, they may also build psycho-emotional resources for supporting these investments.
Multi-Classification of Cell Lines using DHM and Machine Learning

We aim to achieve high performance classification of four cell lines, two breast cancer and two non-cancer, with varied epithelial and mesenchymal morphologies, and to determine the best machine learning model and to identify features most strongly influencing model performance.
IDENTIFICATION OF FACTORS DETERMINING THE CHOICE OF STUDENTS OF PEDAGOGICAL CLASSES OF THEIR PROFESSIONAL TRAJECTORY

The article presents the results of a study of internal and external factors influencing the choice of professional trajectory by graduates of pedagogical classes. The data obtained formed the basis for the development of the content of teaching in pedagogical classes, which helps to stimulate students’ interest in pedagogical activity, the formation of readiness to study at a pedagogical university.
Gaussian Number Theory: $$\mathbb {Z}[i]$$ of the Storm

We have in our mid a veritable maelstrom of pedagogical threads: what is a number? How do we solve Diophantine equations? How do the Fundamental Theorem of Arithmetic and surrounding notions generalize to more exotic number systems? What role does modular arithmetic have to play? The ring $$\mathbb {Z}[i]$$ , recurring in every chapter thus far, turns out to be the eye of this storm, providing us shelter to collect our thoughts on all of these fronts before we venture out into the awe-inspiring wild. With the usual caveat that any given Diophantine equation is usually just a convenient placeholder for the study of something deeper, let us choose to focus for the time being on the Diophantine equations $$x^2+y^2=n$$ for various integers n, asking which numbers n can be written as the sum of two integer squares (and how to do so).
Klasifikasi Berita Hoaks Covid-19 Menggunakan Kombinasi Metode K-Nearest Neighbor dan Information Gain

News is one of information resources that is being used by the public. However, not all news circulating in digital media are facts. Some people take the opportunity to share unfounded and irresponsible news. Since the Covid-19 pandemic hit Indonesia, hoax news about the pandemic has increasingly circulated in digital media. In this study, the author builds a model that can classify hoax news using the K-Nearest Neighbor method combined with the Information Gain feature selection. The data used are factual news data and hoax news data in Indonesian language. Evaluation is done by measuring the performance of the K-Nearest Neighbor model without feature selection and model performance by implementing Information Gain feature selection. The K-Nearest Neighbor model without feature selection with a value of k=5 obtained precision, recall, F1-Score, and accuracy performance of 87.5%, 96.5%, 91.8%, and 91.6%, respectively. While the K-Nearest Neighbor model with a combination of 0.5% Information Gain threshold feature selection with a value of k=3 obtained precision, recall, F1-Score, and accuracy performance of 93.3%, 96.6%, 95%, and 95%, respectively.
Predictive Information Preservation via Variational Information Bottleneck for Cross-View Geo-Localization

Cross-view geo-localization task, which is to handle the problem of matching two images captured same target building, but from different viewpoints, e.g., satellite-view and drone-view, has received significant attention in recent years. However, this research is impeded by the large visual appearance changes across different views and irrelevant content contained in the background. Previous work mitigates the geo-view gap by some similarity-based constraints or utilizing rich contextual information near the target as auxiliary information. Despite some promising breakthroughs made by such methods, they fail to consider the involvement of irrelevant features retained in the high-dimensional features, which reduces the accuracy of the retrieval result. This paper proposes a simple and efficient model termed Predictive Information Preservation Bottleneck (PIPB), using the variational information bottleneck to discard the irrelevant information and retain the predictive information, enhancing the result performance. In particular, our proposed PIPB consists of two stages. Firstly, we learn the part-based features of each image to make full use of neighbor clues, which is realized by the square-ring partition strategy. Then, at the second stage, these learned representations are fed through the variational information bottleneck module to filter out superfluous information. This step can promote the robustness and generalization of our model and improve experiment performance. Extensive experiments are conducted on the recently-released dataset University-1652 and the fundamental benchmark CVACT, showing remarkable performance results compared to other competitive methods.
Retrieval-Then-Parsing: A Two-Stage Model for SQL Generation in Financial Domain

Querying financial databases with natural language has been a strong requirement for financial company staff in recent years. The common method for this task is to transform the natural language into structured query language (SQL), which is referred to as semantic parsing. In this paper, we propose a two-stage model for semantic parsing in large financial databases. The two-stage model consists of an integrated table retriever that is used to retrieve related tables from a large database and a knowledge-enhanced semantic parser that utilizes a knowledge base for SQL generation. Experimental results show that our model achieves decent performance, which ranks the first place on both development and test datasets of CCKS 2022 evaluation task 11.
A Study on Path Planning for Autonomous Drones Considering Wind Risk

In recent years, the use of drones has been advancing in various fields. In particular, attention is focused on autonomous drones that do not require a driver. However, autonomous drones need to use external information acquired by sensors to control flight so that the drone does not crash or become incapable of performing missions. In drones, it is important to consider the effects of wind-induced attitude changes, especially crashes, as well as preventing approach and collision with structures. However, as far as the author knows, path planning considering the influence of wind has not been announced so far. Therefore, we propose a method for path planning that avoids windy areas that have a large effect on drones. In this study, it is assumed that the drone can measure the current position and the wind direction and speed at that point. Based on that information, Model Predictive Control (MPC) performs path planning that predicts the wind condition around the drone and avoids windy areas. In this paper, we explain the wind prediction model and the setting of wind constraints for MPC. We also conducted an autonomous flight simulation in a windy environment to verify the effectiveness of the proposed method.
IMPROVING STUDENTS' LISTENING SKILL THROUGH STORYTELLING APPLICATION IN SMK NEGERI 5 SAMARINDA

Learning that uses technology in the form of learning applications that are used as a means for teachers and students to facilitate the learning process that aims to achieve the success of a learning and also create an effective learning atmosphere, learning by using mobile learning media can make students interesting and fun. The researcher took one of the learning application technologies that could be useful for students to achieve English language learning, especially in listening, which was the story telling application. This application can be downloaded via Google Play Store and can be used by students to explain or describe listening skills in English. This application provides various interesting stories with English vocabulary and pronunciation that are easily understood by Indonesian students. The objective of this study is to find out is there any effect of Improving Students' Listening Skill Through Storytelling Application in SMK Negeri 5 Samarinda. This research used Classroom Action Research (CAR). The result showed that the use of storytelling applications to improve the listening skills of class XI students of SMK Negeri 5 Samarinda was successful. The students were very enthusiastic when the researcher asked the students to fill in the worksheet with questions to complete the story together
Central Bank Digital Currency and Financial Inclusion

Boosting financial inclusion is one of the main motivations for issuing retail central bank digital currencies (CBDCs) among developing economies. In this paper, we develop a model incorporating the impact of financial inclusion to study the implications of introducing CBDC. One of the most frequently raised concerns regarding CBDC issuance is the disintermediation of banks and impact on the overall supply of credit. However, CBDCs in developing economies (unlike in advanced economies) have the potential to bank large unbanked populations and boost financial inclusion which can increase overall lending and reduce bank disintermediation risks. Our model captures two key channels. First, CBDC issuance can increase bank deposits from the previously unbanked by incentivizing the opening of bank accounts for access to CBDC wallets (offsetting potential flows from deposits to CBDCs among those already banked). Second, CBDC usage allows for the building of credit to reduce credit-risk information asymmetry in lending. We find that CBDC increases aggregate household welfare and overall lending if (1) the size and relative wealth of the previously unbanked population is large, (2) CBDC is valuable as a means of payment, (3) CBDC usage allows banks to distinguish between consumers with large differences in credit risk, and (4) bank deposit liquidity risk is low. CBDC can still be optimal for household welfare when overall lending decreases if the value of CBDC as a means of payment is high. Most countries are considering a "two-tier" CBDC model, where central banks issue CBDC to commercial banks which in turn distribute them to consumers. Under an alternative "two-tier" model where non-bank payment system providers (PSPs) can distribute CBDC, fewer funds will flow into deposit accounts from the unbanked because a bank account is no longer needed to access CBDC. If CBDC data is shareable with commercial banks, those without bank accounts can still build credit and access lower interest rate loans. This design is optimal for household welfare if the gains from greater access to CBDC outweigh the contraction in lending.
Interface for AMR by Recognition of Pointing Direction based on Joint Positions Estimated by using RGB-D Image

This paper describes a pointing gesture interface for an AMR used in factory transportation. The pointing gesture by a person is not always precise and contains ambiguity because of the person’s habit. Previous research on pointing gestures has focused on estimating the exact pointing position, which affects the accuracy of robot guidance. Our research assumes that human pointing is ambiguous. Our interface avoids estimating the 2D position on the floor from human pointing, and only estimates the pointing direction. By combining the recognized pointing direction with object recognition using RGB images, our system could guide the AMR to a specific position.
Underwater Image Restoration Based on Local Depth Information Prior

Underwater images often suffer from color distortion and loss of contrast. This is due to the absorption and scattering of light as it travels through water. Although the physical process of underwater imaging is similar to that of haze images in the air. However, traditional dehazing methods cannot produce good results due to the different attenuation of light under different wavelengths in underwater conditions. To overcome this problem, we propose a novel underwater image restoration method based on local depth information priors. First, we use a computer vision-based multi-view geometry method to estimate the local depth information of the image for parameter estimation of the depth compensation model. According to the characteristics of underwater optical imaging, we introduce an underwater color correction method using depth compensation. Second, we propose a method for estimating the global depth image with local depth information priors. Finally, we adopt the global depth image to recover the underwater image. Experimental results demonstrate that the recovered images can achieve better visual quality of underwater images compared to several state-of-the-art methods.
Principle Verification Study of Load-Sensitive Gravity Compensation Mechanism

We aim to develop a new gravity compensation mechanism using a counterweight system. As the load to be compensated changes, the counterweight moves to the equilibrium position to maintain balance. In addition, the center of gravity of the device equipped with the proposed mechanism can be kept within a very narrow range, thus reducing the risk of tipping over. In this paper, we fabricated a prototype of the proposed mechanism, verified the principle of the mechanism from the prototype, and examined the possibility of mounting the mechanism on the main body.
A Study on History and Value of Korean Diplomatic Missions Overseas in Korean Modern and Contemporary Era

This thesis intended to examine the ‘Korean cultural heritage value’ targeting Korean diplomatic missions overseas as the concept of ‘Korean cultural heritage’ in the ｢Cultural Heritage Protection Act｣, revised in March 2017, is expanded.&#x0D; During the Joseon Dynasty and the Korean Empire, resident legations were opened and operated in the capitals of major countries such as Japan and the United States. In 1949, through the revision of Presidential Decree No. 60 “Diplomats and Consulates”, the term “Korean Diplomatic Missions Overseas” began to be used.&#x0D; Korean diplomatic missions overseas can be classified into diplomatic, architectural, and landmark values according to the ‘Korean cultural heritage value’. Accordingly, the author introduced the ‘Korean cultural heritage value’ of Korean diplomatic missions overseas that fit the concept of Korean cultural heritage overseas in the ｢Cultural Heritage Protection Act｣ and conducted research for the purpose of utilizing and promoting Korean cultural heritage by discovering valuable Korean diplomatic missions overseas in the future.
WiP: EventTracker-Event Driven Evidence Collection for Digital Forensics

Digital forensics involves credible evidence collection from digital assets and analysis to conclusively attribute events to users and sources. Traditional forensic methods only focus on preserving the evidence and audit trail generated. Further they have the standard practices for evidence collection by invoking these methods manually. In this paper, we present EventTracker which has the features of traditional methods to monitor and track file system and user activity, and can also dynamically invoke evidence collection based on events of interest. EventTracker allows the user to specify the kind of evidence required for an event type giving more flexibility to the user. It also allows users to define custom event types and monitor the system and evidence be logged safely. We implement a proof of concept code of EventTracker integrating several open source facilities and also furnish details of experiments with a handful of custom event types. We also perform a measurement study with file monitoring and quantify the frequency and number of changes typical system operations do to the underlying file system and conclude that the number of changes is often high which warrants automated techniques for investigation.
An enhanced harmony search integrated with adaptive mutation strategy

Aiming at making improvements on solutions to function optimisation problems, an enhanced harmony search, called EHS, is proposed by hybridising differential mutation strategies. EHS employs differential mutation strategies after a solution generated by harmony search, then the solution is integrated into the differential mutation strategies as a target or current vector. Moreover, four differential mutation operators, including target-to-rand/1, target-to-rand/2, target-to-best/1 and target-to-best/2 are invoked adaptively in a random way. Extensive experiments on CEC2014 benchmark functions demonstrate EHS is effective and efficient with the combination of harmony search and the differential mutation strategies.
Rethinking Classification of Oriented Object Detection in Aerial Images

With the help of the rapid development of technology, especially the prevalence of UAVs (unmanned aerial vehicles), object detection in aerial images gains much more attention in computer vision and deep learning.However, traditional methods use horizontal bounding boxes for object representation leading to inconsistency between objects and features.Therefore, many detectors are being built to tackle this problem, and normally they use the conventional approaches of training and testing to achieve the results.Our pipeline proposed to strengthen not only the classification but also localization via independent training processes using convex-hull transformation in data pre-processing phase.We experimented with the well-designed S2ANet, R3Det, ReDet, RoI Transformer and Oriented R-CNN on the well-established oriented object detection dataset DOTA.Then we adopt the best detectors with the well-known classification network EfficientNet to our proposed pipeline and achieve promising results on the oriented object detection DOTA dataset.Moreover, our pipeline can flexibly be adapted to various oriented object detection baselines improving the results in classification via independent extensive training cycles.
Catatan Penilaian Ketercapaian Perguruan Tinggi dalam Menerapkan Pembelajaran Jarak Jauh (E-Learning) di Universitas Pamulang, Universitas Terbuka, dan Universitas Bina Nusantara

The research conducted by the researcher aims to find information regarding the readiness of students in distance learning using the e-learning lecture model, the process of implementing e-learning learning in teaching and learning activities, the inhibiting factors for using online lectures. Thus, the results of the study showed that students were ready to accept the online learning process and their readiness in the application of ICT, however, several obstacles were found in the distance lecture process, such as the internet signal network that was not supported and adequate so that the process of implementing online lectures was not optimal.It was found that the aspects that became obstacles in distance learning using e-learning, which found 3 factors in it, namely: the lack of knowledge dissemination about e-learning lectures was less intense, there was still at least the use of tools and features provided in the e-learning model and still found "404 not found" because the destination website is experiencing problems. Keywords: Evaluation; E-learning; BINUS; UT; UNPAM
Secure Reversible Data Hiding in Encrypted Images based on Classification Encryption Difference

This paper introduces an algorithm to improve the security, efficiency, and embedding capacity of reversible data hiding in encrypted images (RDH-EI). It is based on classification encryption difference and adaptive fixed-length coding. Firstly, the prediction error image is obtained, the difference with a bin value greater than the encryption threshold in the difference histogram is found, and it is further modified to obtain the embedding threshold range. Then, under the condition of ensuring that the difference inside and outside the embedding threshold range is not confused, the difference within the threshold is only scrambled, and the difference outside the threshold is scrambled and mod encrypted. After obtaining the encrypted image, an adaptive difference fixed-length coding method is proposed to encode and compress the differences within the threshold. The secret data is embedded in the multiple most significant bits of the encoded difference. Experimental results show that the embedding capacity of the proposed algorithm is improved compared with the state-of-the-art algorithm.
Meningkatkan Kemampuan Menulis Puisi Menggunakan Metode Partisipatori melalui Media Gambar pada Siswa Kelas X IPA Semester II SMAN 9 Sarolangun Tahun Pelajaran 2020/2021

This research is qualitative descriptive research with a type of class action research (PTK). Data collection is done with observation sheets and tests of student learning outcomes. The purpose of this study is to describe the ability to write poetry using participatory methods through image media in students of class X IPA Semester II SMAN 9 Sarolangun School Year 2020/2021 in an effort to improve poetry writing skills. The object of this study is writing poetry. The subjects in this study were students of class X IPA Semester II SMAN 9 Sarolangun School Year 2020/2021. The results of the study from data from 21 students, 18 students (85.7%) have met the completion of learning (school KKM) with an average score above 70 and 3 students (14.3%) who did not meet the completion of learning. If you look at it as a whole, with an average score of 82.14 obtained by students, it is clear that classically learning is complete. This percentage amount shows that the learning process in improving the ability to write poetry using participatory methods by using image media in cycle 2 is already underway to the maximum. Therefore, no further repair cycles are needed.