Reviewerly/exHarmony huggingface dataset:
Prediction System for Credit Eligibility Using C4.5 Algorithm

Independent Multipurpose Cooperative, Tasti, Pagar Alam City, currently granting credit, the data collection process for the selection of prospective members and prospective credit recipients is still done manually, so they must come to the cooperative to find out whether it is still eligible to receive credit or not, officers must evaluate the data. is to form a decision tree classification model for creditworthiness in cooperatives. The analytical method with the C4.5 Algorithm, the C4.5 Algorithm is one of the algorithms for performing data mining using data classification techniques. The C4.5 algorithm is used to search for knowledge in the data, by finding the relationship between input variables or criteria attributes with target variables or decision attributes. The resulting knowledge is converted into the form of a decision tree so that it is easy to understand. The C4.5 algorithm was chosen because it can make a decision to be able to predict whether a customer is eligible or not to be given credit. The attribute used is income with specified criteria. The results obtained from this study are predictive information on the feasibility of providing credit to cooperatives using the C4.5 algorithm by getting an accuracy value of 93% with an AUC value of 0.898 where this classification is classified as Good Classification or good.
Determining The Location Of RMU, Using K-Means Clustering, Evaluate The Location Of Existing RMU, Using R-Programming

Rice milling into rice, (Rice Milling Unit, RMU) is needed to process the harvested grain into rice. In determining the location, the consideration of road access to consumers is still used and it is often far from the rice fields, the source of the grain material to be milled. This paper aims to analyze the ideal location with the consideration of being in a rice field cluster and the area of the rice field, so that it can accommodate crop yields around the cluster, namely the location of the spatial coordinates and the area (hectare). Primary data was obtained by the author from the Department of Agriculture of Malang Regency and the data was processed as input for analysis. Analysis of determining the location of the center of the rice field cluster using the Weighted K-Means Clustering method by comparing several alternative cluster points (K) and the area of rice fields, testing the variation of K is, K = 5, 6, 7 and 8. Optimal test results at K = 7. Calculations using the R language. The test results obtained that the cluster center suggested 7 clusters to accommodate the rice harvest in 7 regions, namely in the west, north (1), middle (2), south (3), east (1) and west (1 ) . Evaluation of the location of the existing RMU, there are 5 locations, so that for the next planning, you can add 2 more clusters to build the RMU and accommodate a more even rice harvest.
Design and Development of Documentary Video in Batam as Dark Tourism Promotion Media

Tourist destinations are zones that are directly designated and advertised as tourist destinations, and all tourism products are sold by a specific group of tourists who wish to visit a tragic place and know the meaning of the event. It is arranged. , happened in this place. The development of dark tourism can increase foreign tourist visits which can be used as an economy related to the government by attracting foreign tourists who want to provide foreign exchange for dark tourism, including remote areas. I can do that. The author uses the flow of literature study and subsequent problem-solving. This continues and is improved up to the implementation stage and ends at the dissemination stage. There are still many who don't understand how to attract tourists' attention from dark tourist destinations, whether it's shooting a film in the form of a vintage color gradation video with a frame, or using R&amp;D using R&amp;D. Check out the research flow by starting research and dividing the time, looking for stories about the place, and news about the ghost town of Batam calm and explore with the team of ghost hunters of Batam city. While exploring the ghost places of Batam City, unexpected things and events such as camera battery running low while recording, the crew being disturbed by ghosts, and things that are not nearby are crime scenes. Events that require you to bring safety equipment and lighting, as this is an unplanned event.
Pemanfaatan Perpustakaan Sekolah Sebagai Sumber Belajar Melalui LI-BAS KO-MAN-DO

School’s library has the some functions. One of the school’s library functions is as the source of learning. Students literate reading and writing at the library as library’s service. But not all the students have the same competencies to do basic literacy. Consequently, the students are not able to use the library as source of learning. The students have the lack of understanding in basic literacy but they are in junior high school. So that, the teacher designs the technique in learning basic literacy (LIterasi Baca tuliS) or LI-BAS by using consultation, independence, moral suggestion (KOnsultasi, MANdiri, DOrongan moral) or Ko-Man-Do. The goal of the research is helping the students who has lacked of literacy to solve their problems in learning especially reading and writing in simply. The method of the research applies qualitative-description by describing the condition of discussing objects in preceding and post condition. The data collection is by using observation and interview. The data be analyzed by description of reading and writing competencies based on the categories. The research subjects consist of 4 students who have lacked of understanding in basic literacy. The result of research shows that increasing in basic literacy –reading and writing- after application of the Ko-Man-Do technique. The students be able to read words, phrases, sentence and paragraph. The enhancement of the research is about 38% in average. The final discussion reveals that the Ko-Man-Do technique potentially increase student’s ability in basic literacy – reading and writing – in the school’s library.
Recall dan Precision pada Online Public Access Catalog (OPAC) Dinas Arsip dan Perpustakaan Kota Bandung

Disarpus Bandung is a technical institution that has the task and function of developing the habit of reading fondness through library empowerment. The role of the Online Public Access Catalog (OPAC) as an information retrieval system is very supportive as a means or facilities to improve services to the community, especially in the field of libraries in the city of Bandung. This study aims to determine (1) How is recall and precision in the OPAC of the Disarpus Bandung. (2) effectiveness of OPAC as an information retrieval system in the city of Bandung. The data collection method used is by conducting keyword searches on the OPAC of the Disarpus Bandung through the website page http://layanan.dispusip.bandung.go.id/inlislite3/opac/. These 10 keywords were the most searched or searched by library users in March 2022 in Disarpus Bandung. Every keyword used in the search process, the result is a number of catalog data. The catalog data obtained is then analyzed and determined which data are relevant and irrelevant to the information needs sought based on the title, the subject of the book, and the availability of the collection. Then the recall and precision calculations will be carried out to determine the effectiveness of the INLISLite application retrieval. Based on the results of testing on the OPAC of the Disarpus Bandung with 10 keywords most frequently searched by users during March 2022, it can be concluded that the information retrieval system has been effective. This can be seen from the average recall value of 0.96 or 95.79% and the average precision value of 0.92 or 92.31%. Of the 10 keywords, namely nursing, human resources, communication, research methods, accounting, taxation, information systems, skills, parenting, and psychology, the keywords with the highest recall and precision values are research methods and parenting keywords, namely the recall value of 1 or 100% and a precision value of 1 or 100%. Meanwhile, the lowest recall and precision values are communication keywords, with a recall value of 0.90 or 90.34% and a precision value of 0.88 or 88.33%. Overall, the OPAC at the Disarpus Bandung has been effective as an information retrieval system.
A Study on the Determinants of Green ODA Distribution Focusing on OECD Development Assistance Committee

본 논문의 목적은 OECD DAC 공여국의 녹색 ODA 배분 동기를 분석하는 데 있다. 많은 선행연구들은 녹색 ODA의 배분 동기를 공여국의 이익(DI) 또는 수원국의 필요(RN)의 관점에서 분석해왔다. 그러나 이는 국가 간 환경협력의 질적 강화나 국제사회의 규범 등 외생적 구조의 영향력을 설명할 수 없으며, 국내 정치체제를 일종의 블랙박스로 간주함에 따라 녹색 ODA 배분을 둘러싼 맥락을 충분히 설명하지 못하는 한계가 있다. 따라서 본 연구는 기존의 논의와 함께 세계사회 및 국내정치 체제의 영향력을 검증하고자 하였다. 분석결과, 국제 규범의 영향, ISO 14000 인증기업의 수, 그리고 정부의 이념이 진보적일수록 녹색 ODA의 배분에 긍정적인 영향을 미치는 것으로 나타났다. 이러한 결과는 녹색 ODA의 배분에서 국제사회의 규범에 대한 중요성과 국내정치 체제에 대한 고려가 필요하다는 점을 시사한다. The purpose of this research is to analyze the motivation for green ODA distribution in OECD DAC countries. Many previous studies have analyzed the motivation for the distribution of green ODA in terms of the interests of donor countries(DI) or the needs of recipient countries (RN). However, this cannot explain the influence of exogenous structures such as strengthening the quality of environmental cooperation between countries and norms of the international community, and there is a limit to not fully explaining the context surrounding green ODA distribution as the domestic political system is regarded as a kind of black box. Therefore, this study attempted to verify the influence of the global society and domestic political system along with the existing discussions. As a result of the analysis, it was found that the more progressive the influence of international norms, the number of ISO 14000 certified companies, and the more progressive the government"s ideology, the more positive the allocation of green ODA. These results suggest that the importance of the international community"s norms in the distribution of green ODA and the need to consider the domestic political system.
A Study on the Expansion of Support for Culture and Arts by the Cultural Foundation of Private Enterprises: Focusing on the Samsung Cultural Foundation

공공재로써 외부 지원이 필수적으로 수반되고 있는 문화예술 분야에서 기업 문화재단의 역할과 관련하여 다양한 논의가 전개되고 있다. 특히 대기업이 설립한 문화재단은 건실한 재정을 앞세워 민간 문화예술 지원 기구의 기능을 하고 있으나, 지원 장르의 편중성 등 운영의 한계를 노정하고 있다. 본 연구는 이러한 현실을 반영하여 국내 최초의 기업 문화재단으로 최대 규모의 자산을 보유한 삼성문화재단의 운영상 문제점을 살펴보고 문화예술 분야 지원 확대를 위한 효율적 운영 전략을 제시하는 데 목적이 있다. 연구를 통해 첫째, 미술관 운영 전문 재단으로서의 지속성 여부에 대한 논의 필요성, 둘째, 문화예술 분야 지원의 사각지대 해소를 위한 적극적 행보, 셋째, 정부 또는 지방자치단체와의 전략적 제휴, 넷째, 리움 · 호암미술관 두 곳 중 한 곳에 대한 복합문화공간 조성 검토를 결론으로 제시하였다. Various discussions are taking place regarding the role of the Corporate Cultural Foundation in the field of culture and arts, where external support is essential as a public good. In particular, the Cultural Foundation, established by large corporations, functions as a private culture and arts support organization with sound finances, but is facing limitations in operation, including the bias of the support genre. Reflecting this reality, this study aims to examine the operational problems of the Samsung Cultural Foundation, which has the largest assets as the first corporate cultural foundation in Korea, and to present an efficient operation strategy to expand support in the culture and arts field. Through the study, first, the need to discuss sustainability as an art museum management foundation, second, active steps to resolve blind spots in the culture and arts sector, third, strategic alliances between the Samsung Cultural Foundation and the government or local governments, and fourth, a review of the creation of a complex cultural space.
Is Policy Learning Overrated?: Width-Based Planning and Active Learning for Atari

Width-based planning has shown promising results on Atari 2600 games using pixel input, while using substantially fewer environment interactions than reinforcement learning. Recent width-based approaches have computed feature vectors for each screen using a hand designed feature set or a variational autoencoder trained on game screens (VAE-IW), and prune screens that do not have novel features during the search. We propose Olive (Online-VAE-IW), which updates the VAE features online using active learning to maximize the utility of screens observed during planning. Experimental results in 55 Atari games demonstrate that it outperforms Rollout-IW by 42-to-11 and VAE-IW by 32-to-20. Moreover, Olive outperforms existing work based on policy-learning ($\pi$-IW, DQN) trained with 100x training budget by 30-to-22 and 31-to-17, and a state of the art data-efficient reinforcement learning (EfficientZero) trained with the same training budget and ran with 1.8x planning budget by 18-to-7 in Atari 100k benchmark, with no policy learning at all. The source code is available at github.com/ibm/atari-active-learning .
SsAG: Summarization and sparsification of Attributed Graphs

We present SsAG, an efficient and scalable lossy graph summarization method that retains the essential structure of the original graph. SsAG computes a sparse representation (summary) of the input graph and also caters to graphs with node attributes. The summary of a graph $G$ is stored as a graph on supernodes (subsets of vertices of $G$), and a weighted superedge connects two supernodes. The proposed method constructs a summary graph on $k$ supernodes that minimize the reconstruction error (difference between the original graph and the graph reconstructed from the summary) and maximum homogeneity with respect to attributes. We construct the summary by iteratively merging a pair of nodes. We derive a closed-form expression to efficiently compute the reconstruction error after merging a pair and approximate this score in constant time. To reduce the search space for selecting the best pair for merging, we assign a weight to each supernode that closely quantifies the contribution of the node in the score of the pairs containing it. We choose the best pair for merging from a random sample of supernodes selected with probability proportional to their weights. A logarithmic-sized sample yields a comparable summary based on various quality measures with weighted sampling. We propose a sparsification step for the constructed summary to reduce the storage cost to a given target size with a marginal increase in reconstruction error. Empirical evaluation on several real-world graphs and comparison with state-of-the-art methods shows that SsAG is up to $5\times$ faster and generates summaries of comparable quality.
Focused Contrastive Training for Test-based Constituency Analysis

We propose a scheme for self-training of grammaticality models for constituency analysis based on linguistic tests. A pre-trained language model is fine-tuned by contrastive estimation of grammatical sentences from a corpus, and ungrammatical sentences that were perturbed by a syntactic test, a transformation that is motivated by constituency theory. We show that consistent gains can be achieved if only certain positive instances are chosen for training, depending on whether they could be the result of a test transformation. This way, the positives, and negatives exhibit similar characteristics, which makes the objective more challenging for the language model, and also allows for additional markup that indicates the position of the test application within the sentence.
A simple Sign-Bit Probabilistic Shaping Scheme

We propose a new shaping scheme for the Gaussian channel whose complexity is approximately half the one of a binary distribution matcher (DM). The result is obtained as follows: We first show that most of the shaping gain can be obtained via a simplified version of sign-bit shaping, which uses only two non-uniform binary sources. This is achieved by considering a stepwise Maxwell-Boltzmann-like distribution of the symbols. One of the two binary sources has a parameter $p$ close to 0. Hence, we then describe a binary DM which explicitly takes advantage of this aspect and has a negligible complexity. Since the two binary sources are used alternately with equal probability, the complexity of the proposed shaping scheme is half the one of the second binary DM.
RED++ : Data-Free Pruning of Deep Neural Networks via Input Splitting and Output Merging

Pruning Deep Neural Networks (DNNs) is a prominent field of study in the goal of inference runtime acceleration. In this paper, we introduce a novel data-free pruning protocol RED++. Only requiring a trained neural network, and not specific to DNN architecture, we exploit an adaptive data-free scalar hashing which exhibits redundancies among neuron weight values. We study the theoretical and empirical guarantees on the preservation of the accuracy from the hashing as well as the expected pruning ratio resulting from the exploitation of said redundancies. We propose a novel data-free pruning technique of DNN layers which removes the input-wise redundant operations. This algorithm is straightforward, parallelizable and offers novel perspective on DNN pruning by shifting the burden of large computation to efficient memory access and allocation. We provide theoretical guarantees on RED++ performance and empirically demonstrate its superiority over other data-free pruning methods and its competitiveness with data-driven ones on ResNets, MobileNets and EfficientNets.
Span Labeling Approach for Vietnamese and Chinese Word Segmentation

In this paper, we propose a span labeling approach to model n-gram information for Vietnamese word segmentation, namely SPAN SEG. We compare the span labeling approach with the conditional random field by using encoders with the same architecture. Since Vietnamese and Chinese have similar linguistic phenomena, we evaluated the proposed method on the Vietnamese treebank benchmark dataset and five Chinese benchmark datasets. Through our experimental results, the proposed approach SpanSeg achieves higher performance than the sequence tagging approach with the state-of-the-art F-score of 98.31% on the Vietnamese treebank benchmark, when they both apply the contextual pre-trained language model XLM-RoBERTa and the predicted word boundary information. Besides, we do fine-tuning experiments for the span labeling approach on BERT and ZEN pre-trained language model for Chinese with fewer parameters, faster inference time, and competitive or higher F-scores than the previous state-of-the-art approach, word segmentation with word-hood memory networks, on five Chinese benchmarks.
Embedding in MDS codes and Latin cubes

An embedding of a code is a mapping that preserves distances between codewords. We prove that any code with code distance $ρ$ and length $d$ can be embedded into an MDS code with the same code distance and length but under a larger alphabet. As a corollary we obtain embeddings of systems of partial mutually orthogonal Latin cubes and $n$-ary quasigroups.
Compositional generalization in semantic parsing with pretrained transformers

Large-scale pretraining instills large amounts of knowledge in deep neural networks. This, in turn, improves the generalization behavior of these models in downstream tasks. What exactly are the limits to the generalization benefits of large-scale pretraining? Here, we report observations from some simple experiments aimed at addressing this question in the context of two semantic parsing tasks involving natural language, SCAN and COGS. We show that language models pretrained exclusively with non-English corpora, or even with programming language corpora, significantly improve out-of-distribution generalization in these benchmarks, compared with models trained from scratch, even though both benchmarks are English-based. This demonstrates the surprisingly broad transferability of pretrained representations and knowledge. Pretraining with a large-scale protein sequence prediction task, on the other hand, mostly deteriorates the generalization performance in SCAN and COGS, suggesting that pretrained representations do not transfer universally and that there are constraints on the similarity between the pretraining and downstream domains for successful transfer. Finally, we show that larger models are harder to train from scratch and their generalization accuracy is lower when trained up to convergence on the relatively small SCAN and COGS datasets, but the benefits of large-scale pretraining become much clearer with larger models.
Real-time dynamics of Plaquette Models using NISQ Hardware

Practical quantum computing holds clear promise in addressing problems not generally tractable with classical simulation techniques, and some key physically interesting applications are those of real-time dynamics in strongly coupled lattice gauge theories. In this article, we benchmark real-time dynamics of $\mathbb{Z}_2$ and $U(1)$ gauge invariant plaquette models using superconducting-qubit based quantum IBM Q computers. We design quantum circuits for models of increasing complexity and measure physical observables such as the return probability to the initial state, and locally conserved charges. Even though the quantum hardware suffers from decoherence, we demonstrate the use of error mitigation techniques, such as circuit folding methods implemented via the Mitiq package, and show what they can achieve within the quantum volume restrictions for the IBM Q quantum computers. Our study provides insight into the choice of quantum hardware topologies, construction of circuits, and the utility of error mitigation methods to devise large-scale quantum computation strategies for lattice gauge theories.
Non-parametric calibration of multiple related radiocarbon determinations and their calendar age summarisation

Due to fluctuations in past radiocarbon ($^{14}$C) levels, calibration is required to convert $^{14}$C determinations $X_i$ into calendar ages $\theta_i$. In many studies, we wish to calibrate a set of related samples taken from the same site or context, which have calendar ages drawn from the same shared, but unknown, density $f(\theta)$. Calibration of $X_1, \ldots, X_n$ can be improved significantly by incorporating the knowledge that the samples are related. Furthermore, summary estimates of the underlying shared $f(\theta)$ can provide valuable information on changes in population size/activity over time. Most current approaches require a parametric specification for $f(\theta)$ which is often not appropriate. We develop a rigorous non-parametric Bayesian approach using a Dirichlet process mixture model, with slice sampling to address the multimodality typical within $^{14}$C calibration. Our approach simultaneously calibrates the set of $^{14}$C determinations and provides a predictive estimate for the underlying calendar age of a future sample. We show, in a simulation study, the improvement in calendar age estimation when jointly calibrating related samples using our approach, compared with calibration of each $^{14}$C determination independently. We also illustrate the use of the predictive calendar age estimate to provide insight on activity levels over time using three real-life case studies.
Worrisome Patterns in Developers: A Survey in Cryptography

We surveyed 97 developers who had used cryptography in open-source projects, in the hope of identifying developer security and cryptography practices. We asked them about individual and company-level practices, and divided respondents into three groups (i.e., high, medium, and low) based on their level of knowledge. We found differences between the high-profile developers and the other two groups. For instance, high-profile developers have more years of experience in programming, have attended more security and cryptography courses, have more background in security, are highly concerned about security, and tend to use security tools more than the other two groups. Nevertheless, we observed worrisome patterns among all participants such as the high usage of unreliable sources like Stack Overflow, and the low rate of security tool usage.