Reviewerly/exHarmony huggingface dataset:
ANALISIS SURVIVAL MENGGUNAKAN METODE REGRESI COX PASIEN PENDERITA TIFUS DI RSUD SYEKH YUSUF KABUPATEN GOWA

This research is an applied researchs with a quantitative approach, namely taking or collecting the necessary data and analyzing it using the cox regression model with proportional hazards to determine the factors that affect the recovery rate of typhoid patients at Syekh Yusuf Hospital.In Gowa.The long treatment of typhoid patients is the survival time.In accordance with the Anderson Darling test using Minitab 15 software, the test results on the distribution of survival time of typhoid patients is the Gamma Distribution.Many factors will affect the patient's recovery rate such as the patient's age, patient's gender, dizziness, dirty tongue, heartburn, nausea, vomiting, and diarrhea.Therefore, it is important to know what factors most influence the recovery rate of typhoid patients.From the results of research using SPSS 20 software, it was concluded that the factors that influence the healing time of typhoid patients at Syekh Yusuf Gowa Hospital are the patient's age.
Development of Decision Support Applications for Granting Mortgages using the Fuzzy-Weighted Product method

Kredit Pemilikan Rumah adalah salah satu fasilitas yang diberikan bank atau Lembaga keuangan lainnya. Kredit pemilikan rumah ini biasanya diperuntukkan bagi masyarakat yang kesulitan dalam membeli rumah. Maka dari itu adanya fasilitas ini diharapkan dapat membantu masyarakat dalam membeli rumah secara kredit. Permasalahan yang terjadi adalah seleksi yang dilakukan oleh analis kredit masih dilakukan secara subyektif sehingga terkadang analis kredit masih kesulitan dalam menentukan layak atau tidaknya nasabah tersebut menerima kredit. Tujuan dari penelitian ini adalah membangun sebuah system informasi pendukung keputusan dengan metode Fuzzy-WP guna membantu analis kredit dalam memutuskan pemberian kredit kepemilikan rumah. Hasil implementasi algoritma menunjukkan bahwa Fuzzy-WP yang diterapkan pada sistem telah sesuai dengan perhitungan aslinya serta program aplikasi dapat menampilkan hasil rekomendasi sesuai dengan kriteria yang telah ditetapkan untuk pemberian kredit bagi calon debitur.&#x0D; &#x0D; Home Ownership Credit (KPR) is one of the facilities provided by banks or other financial institutions. These home ownership loans are usually reserved for people who have difficulty buying a home. Therefore, the existence of this facility is expected to assist people in buying houses on credit. The problem that occurs is that the selection made by credit analysts is still carried out subjectively so that sometimes credit analysts still have difficulty determining whether a customer is eligible to receive credit or not. The purpose of this study is to build a decision support information system using the Fuzzy-WP method to assist credit analysts in deciding whether to grant housing loans. The results of the implementation of the algorithm show that the Fuzzy-WP applied to the system is in accordance with the initial calculations and the application program can display recommendation results according to predetermined criteria for granting credit to prospective borrowers.
Optimization of Support Vector Machine and XGBoost Methods Using Feature Selection to Improve Classification Performance

Breast cancer still ranks first in the number of cancers in Indonesia and is one of the contributors to the death rate caused by cancer. Generally occurs in women, but does not rule out it can occur in men. Globocan data in 2020 shows the number of new cancer cases in Indonesia reached 65,858 or 16.6% of the total 396,914 new cancer cases, with the number of deaths reaching 22,430 cases. Early detection can allow patients to get the right therapy and increase their chances of survival. The purpose of this study is to implement a machine learning algorithm to detect breast cancer in women, the algorithms that will be used are Support Vector Machine (SVM) and XBGoost by implementing feature selection to obtain better accuracy. The classification results of the two algorithms will be compared to find out which algorithm has the best performance. The dataset used is from the SEER NCI program in November 2017 involving 4024 patients. The research shows that of the 16 attributes contained in the dataset, there are 3 attributes (features) that have a significant effect on the classification results, namely 6th stage, reginol node positive, and tumor size. XGBoost with feature selection has a better performance of 91.4% compared to SVM which is only 89.8%.
Adaptive Texture Filtering for Single-Domain Generalized Segmentation

Domain generalization in semantic segmentation aims to alleviate the performance degradation on unseen domains through learning domain-invariant features. Existing methods diversify images in the source domain by adding complex or even abnormal textures to reduce the sensitivity to domain specific features. However, these approaches depend heavily on the richness of the texture bank, and training them can be time-consuming. In contrast to importing textures arbitrarily or augmenting styles randomly, we focus on the single source domain itself to achieve generalization. In this paper, we present a novel adaptive texture filtering mechanism to suppress the influence of texture without using augmentation, thus eliminating the interference of domain-specific features. Further, we design a hierarchical guidance generalization network equipped with structure-guided enhancement modules, which purpose is to learn the domain-invariant generalized knowledge. Extensive experiments together with ablation studies on widely-used datasets are conducted to verify the effectiveness of the proposed model, and reveal its superiority over other state-of-the-art alternatives.
Pseudo-label Correction and Learning For Semi-Supervised Object Detection

Pseudo-Labeling has emerged as a simple yet effective technique for semi-supervised object detection (SSOD). However, the inevitable noise problem in pseudo-labels significantly degrades the performance of SSOD methods. Recent advances effectively alleviate the classification noise in SSOD, while the localization noise which is a non-negligible part of SSOD is not well-addressed. In this paper, we analyse the localization noise from the generation and learning phases, and propose two strategies, namely pseudo-label correction and noise-unaware learning. For pseudo-label correction, we introduce a multi-round refining method and a multi-vote weighting method. The former iteratively refines the pseudo boxes to improve the stability of predictions, while the latter smoothly self-corrects pseudo boxes by weighing the scores of surrounding jittered boxes. For noise-unaware learning, we introduce a loss weight function that is negatively correlated with the Intersection over Union (IoU) in the regression task, which pulls the predicted boxes closer to the object and improves localization accuracy. Our proposed method, Pseudo-label Correction and Learning (PCL), is extensively evaluated on the MS COCO and PASCAL VOC benchmarks. On MS COCO, PCL outperforms the supervised baseline by 12.16, 12.11, and 9.57 mAP and the recent SOTA (SoftTeacher) by 3.90, 2.54, and 2.43 mAP under 1\%, 5\%, and 10\% labeling ratios, respectively. On PASCAL VOC, PCL improves the supervised baseline by 5.64 mAP and the recent SOTA (Unbiased Teacherv2) by 1.04 mAP on AP$^{50}$.
Asynchronous Modal FRP

Over the past decade, a number of languages for functional reactive programming (FRP) have been suggested, which use modal types to ensure properties like causality, productivity and lack of space leaks. So far, almost all of these languages have included a modal operator for delay on a global clock. For some applications, however, the notion of global clock is unnatural and leads to leaky abstractions as well as inefficient implementations. While modal languages without a global clock have been proposed, no operational properties have been proved about them, yet. This paper proposes Async RaTT, a new modal language for asynchronous FRP, equipped with an operational semantics mapping complete programs to machines that take asynchronous input signals and produce output signals. The main novelty of Async RaTT is a new modality for asynchronous delay, allowing each output channel to be associated at runtime with the set of input channels it depends on, thus causing the machine to only compute new output when necessary. We prove a series of operational properties including causality, productivity and lack of space leaks. We also show that, although the set of input channels associated with an output channel can change during execution, upper bounds on these can be determined statically by the type system.
Boosting Differentiable Causal Discovery via Adaptive Sample Reweighting

Under stringent model type and variable distribution assumptions, differentiable score-based causal discovery methods learn a directed acyclic graph (DAG) from observational data by evaluating candidate graphs over an average score function. Despite great success in low-dimensional linear systems, it has been observed that these approaches overly exploit easier-to-fit samples, thus inevitably learning spurious edges. Worse still, inherent mostly in these methods the common homogeneity assumption can be easily violated, due to the widespread existence of heterogeneous data in the real world, resulting in performance vulnerability when noise distributions vary. We propose a simple yet effective model-agnostic framework to boost causal discovery performance by dynamically learning the adaptive weights for the Reweighted Score function, ReScore for short, where the weights tailor quantitatively to the importance degree of each sample. Intuitively, we leverage the bilevel optimization scheme to \wx{alternately train a standard DAG learner and reweight samples -- that is, upweight the samples the learner fails to fit and downweight the samples that the learner easily extracts the spurious information from. Extensive experiments on both synthetic and real-world datasets are carried out to validate the effectiveness of ReScore. We observe consistent and significant boosts in structure learning performance. Furthermore, we visualize that ReScore concurrently mitigates the influence of spurious edges and generalizes to heterogeneous data. Finally, we perform the theoretical analysis to guarantee the structure identifiability and the weight adaptive properties of ReScore in linear systems. Our codes are available at https://github.com/anzhang314/ReScore.
Continuous Sign Language Recognition with Correlation Network

Human body trajectories are a salient cue to identify actions in the video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition (CSLR) usually process frames independently, thus failing to capture cross-frame trajectories to effectively identify a sign. To handle this limitation, we propose correlation network (CorrNet) to explicitly capture and leverage body trajectories across frames to identify signs. In specific, a correlation module is first proposed to dynamically compute correlation maps between the current frame and adjacent frames to identify trajectories of all spatial patches. An identification module is then presented to dynamically emphasize the body trajectories within these correlation maps. As a result, the generated features are able to gain an overview of local temporal movements to identify a sign. Thanks to its special attention on body trajectories, CorrNet achieves new state-of-the-art accuracy on four large-scale datasets, i.e., PHOENIX14, PHOENIX14-T, CSL-Daily, and CSL. A comprehensive comparison with previous spatial-temporal reasoning methods verifies the effectiveness of CorrNet. Visualizations demonstrate the effects of CorrNet on emphasizing human body trajectories across adjacent frames.
Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning

Progress in fields of machine learning and adversarial planning has benefited significantly from benchmark domains, from checkers and the classic UCI data sets to Go and Diplomacy. In sequential decision-making, agent evaluation has largely been restricted to few interactions against experts, with the aim to reach some desired level of performance (e.g. beating a human professional player). We propose a benchmark for multiagent learning based on repeated play of the simple game Rock, Paper, Scissors along with a population of forty-three tournament entries, some of which are intentionally sub-optimal. We describe metrics to measure the quality of agents based both on average returns and exploitability. We then show that several RL, online learning, and language model approaches can learn good counter-strategies and generalize well, but ultimately lose to the top-performing bots, creating an opportunity for research in multiagent learning.
AmQA: Amharic Question Answering Dataset

Question Answering (QA) returns concise answers or answer lists from natural language text given a context document. Many resources go into curating QA datasets to advance robust models' development. There is a surge of QA datasets for languages like English, however, this is not true for Amharic. Amharic, the official language of Ethiopia, is the second most spoken Semitic language in the world. There is no published or publicly available Amharic QA dataset. Hence, to foster the research in Amharic QA, we present the first Amharic QA (AmQA) dataset. We crowdsourced 2628 question-answer pairs over 378 Wikipedia articles. Additionally, we run an XLMR Large-based baseline model to spark open-domain QA research interest. The best-performing baseline achieves an F-score of 69.58 and 71.74 in reader-retriever QA and reading comprehension settings respectively.
Multi-User Entanglement Distribution in Quantum Networks Using Multipath Routing

Quantum networks facilitate numerous applications such as secure communication and distributed quantum computation by performing entanglement distribution. Multi-user quantum applications where quantum information is shared between multiple users require access to a shared multipartite state between the users. We consider the problem of designing protocols for distributing such states, at an increased entanglement rate. We propose three protocols that increase the entanglement rate of multi-user applications by leveraging multipath routing. The protocols are evaluated on quantum networks with NISQ constraints, including limited quantum memories and probabilistic entanglement generation. Monte Carlo simulation results show that the developed protocols achieve an exponential speedup of entanglement rate compared to single-path routing techniques, with a maximum speedup of four orders of magnitude for the cases studied. The speedup was also found to improve for larger sets of users. When the protocols were tested in scaled-down real-world topologies, it was found that topology can have a significant effect on the achievable entanglement rates, with one order of magnitude difference between topologies. Finally, we find that the benefits of multipath routing are a maximum for short quantum memory decoherence times, and intermediate values of entanglement generation probability. Hence the protocols developed can benefit NISQ quantum network control and design.
Quantum Algorithm for Path-Edge Sampling

We present a quantum algorithm for sampling an edge on a path between two nodes s and t in an undirected graph given as an adjacency matrix, and show that this can be done in query complexity that is asymptotically the same, up to log factors, as the query complexity of detecting a path between s and t. We use this path sampling algorithm as a subroutine for st-path finding and st-cut-set finding algorithms in some specific cases. Our main technical contribution is an algorithm for generating a quantum state that is proportional to the positive witness vector of a span program.
Organizational Identification: Role of Proactive Vitality Management and Resources in Predicting Organizational Outcomes

The purpose of the current paper is to examine the role of organizational identification in predicting outcomes. Specifically, the researcher takes proactive vitality management and organizational resources as the predictor of identification. Stratified and snowball sampling techniques collect data from the service and manufacturing sectors. Results support the significant relationship of proactive vitality management and resources with organizational identification. Moreover, identification predicts both positive outcomes (innovation behavior, employee voice) and reduces the effect of negative outcomes (workplace ostracism). The current study contributes to the literature by explaining the relational mechanism between proactive vitality management and positive and negative behavior with mediating role of organizational identification. Findings will help managers get a competitive edge by promoting innovative and proactive voice behavior by reducing workplace ostracism.
On the Probabilistic Quantum Error Correction

Probabilistic quantum error correction is an error-correcting procedure which uses postselection to determine if the encoded information was successfully restored. In this work, we analyze the probabilistic version of the error-correcting procedure for general noise. We generalize the Knill-Laflamme conditions for probabilistically correctable errors. We show that for some noise channels, the initial information has to be encoded into a mixed state to maximize the probability of successful error correction. Finally, the probabilistic error-correcting procedure offers an advantage over the deterministic procedure. Reducing the probability of successful error correction allows for correcting errors generated by a broader class of noise channels. Significantly, if the errors are caused by a unitary interaction with an auxiliary qubit system, we can probabilistically restore a qubit state by using only one additional physical qubit.
Evaluation of banknote identification methodologies based on local and deep features

There are many people with disabilities; it is estimated that 39 million people are blind and 246 million have limited vision, giving 285 million visually impaired people. Information and communication technologies can help disabled people achieve greater independence, quality of life, and inclusion in social activities by increasing, maintaining, or improving their functional capacities. This paper presents a significant evaluation of local and deep features for an automatic methodology for identifying banknotes. To determine the best local features, we evaluated a set of four point-of-interest detectors, two descriptors, seven ways of generating the image signature, and six classification methodologies. To define the deep features, we extract features using three pre-trained well-known CNNs. Additionally, we evaluated using a hybrid approach formed by combining local and deep features. In this situation, the features were selected according to their gain ratios and used as input to the classifier. Experiments performed on US dollar (USD), euro (EUR), and Brazilian real banknotes (BRL) obtained accuracy rates of 99.96%, 99.12%, and 96.92%, respectively.
Correction to: Semantic Similarity in a Taxonomy by Evaluating the Relatedness of Concept Senses with the Linked Data Semantic Distance

In an older version of this paper, the online xml is fine but in the pdf the characters > and < are not recognized on page 69. This has been corrected.
IMPACT OF FOREIGN AID ON ECONOMIC GROWTH IN KENYA 2006-2020

Purpose: The objective of this study was to examine the impact of foreign aid on economic growth in Kenya.&#x0D; Methodology: The study used a descriptive research technique and a time series approach to track economic growth in Kenya for 41 years, from the year 1980 to 2020. The study also adopted a census survey to collect secondary data by use of a secondary time series data template from the World Bank and KNBS databases in Kenya for the period 1980 to 2020. The collected data was edited, coded and analyzed using statistical package for social sciences (SPSS).&#x0D; Findings: The results of the study revealed that ODA, emergency &amp; food aid, debt forgiveness and technical cooperation had a positive and significant relationship with economic growth(GDP). In addition, ODA, emergency &amp; food aid and debt forgiveness have a positive and significant impact on economic growth. However, technical cooperation had a negative and no significant impact on economic growth in Kenya&#x0D; Unique Contribution to Theory, Practice and Policy: The study recommended that financial policymakers should come up with better strategies on the management of the foreign aid funds so as to ensure they are efficiently utilized. Financial policy makers should also consider foreign donors who can offer debt forgiveness aid since it will help Kenya in managing and reducing its debts.
Design of Online Art Appreciation Course Teaching System Based on Interactive Scene

The teaching of art appreciation course is quite different from that of other professional courses, and there is a high demand for interaction between teachers and students. In order to solve the defects of the online art appreciation course teaching system and obtain better teaching effect, an online art appreciation course teaching system based on interactive scene is designed. The system hardware design unit includes teaching data processor selection unit, system operation controller selection unit and peripheral circuit design unit. The software module includes student state detection module, interactive scene setting module and database construction module. Through the design and development of the above hardware units and software modules, the operation and application of the online art appreciation course teaching system are realized. The experimental results show that compared with the comparison system without interactive scene, the system response time obtained by using interactive scene design system is shorter and the accuracy of student state recognition is higher, which fully proves that the design system has certain feasibility and effectiveness.
Leg Posture Correction System for Physical Education Students Based on Multimodal Information Processing

The traditional leg posture correction system has the problem that the joint points are arranged in reverse order and connected incorrectly, which affects the accuracy of posture recognition. In response to this problem, this research designed a leg posture correction system for students in physical education class based on multi-modal information processing. In the hardware part of the system, a signal conditioning circuit is used to filter, amplify, and sample the input signal, and an operational amplifier with a zero-adjusting terminal is used in conjunction with a D/A converter to realize the zero-adjustment of the circuit. In the software part of the system, after segmenting the depth image of the scene object containing the viewpoint, establish a database of the leg pose of the students in physical education class, then fuse the data vector and use the multi-modal information processing model to recognize the leg pose, and use the recognition result as the misrecognition probability matrix model The input to realize the intelligent error correction of the wrong leg posture. The experimental results show that under the test condition of 100 users, the positioning accuracy of the leg parts of the system in this paper is as high as 86.85%, which proves that it has a good application effect.