Reviewerly/exHarmony huggingface dataset:
Unfolding Taylor's Approximations for Image Restoration

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image. In practice, however, existing methods empirically construct encapsulated end-to-end mapping networks without deepening into the rationality, and neglect the intrinsic prior knowledge of restoration task. To solve the above problems, inspired by Taylor's Approximations, we unfold Taylor's Formula to construct a novel framework for image restoration. We find the main part and the derivative part of Taylor's Approximations take the same effect as the two competing goals of high-level contextualized information and spatial details of image restoration respectively. Specifically, our framework consists of two steps, correspondingly responsible for the mapping and derivative functions. The former first learns the high-level contextualized information and the later combines it with the degraded input to progressively recover local high-order spatial details. Our proposed framework is orthogonal to existing methods and thus can be easily integrated with them for further improvement, and extensive experiments demonstrate the effectiveness and scalability of our proposed framework.
TREND: Trigger-Enhanced Relation-Extraction Network for Dialogues

The goal of dialogue relation extraction (DRE) is to identify the relation between two entities in a given dialogue. During conversations, speakers may expose their relations to certain entities by explicit or implicit clues, such evidences called "triggers". However, trigger annotations may not be always available for the target data, so it is challenging to leverage such information for enhancing the performance. Therefore, this paper proposes to learn how to identify triggers from the data with trigger annotations and then transfers the trigger-finding capability to other datasets for better performance. The experiments show that the proposed approach is capable of improving relation extraction performance of unseen relations and also demonstrate the transferability of our proposed trigger-finding model across different domains and datasets.
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning

Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data. In this work, we investigate Contrastive Learning (CL), a key component in self-supervised approaches, on HGs to alleviate the label scarcity problem. We first generate multiple semantic views according to metapaths and network schemas. Then, by pushing node embeddings corresponding to different semantic views close to each other (positives) and pulling other embeddings apart (negatives), one can obtain informative representations without human annotations. However, this CL approach ignores the relative hardness of negative samples, which may lead to suboptimal performance. Considering the complex graph structure and the smoothing nature of GNNs, we propose a structure-aware hard negative mining scheme that measures hardness by structural characteristics for HGs. By synthesizing more negative nodes, we give larger weights to harder negatives with limited computational overhead to further boost the performance. Empirical studies on three real-world datasets show the effectiveness of our proposed method. The proposed method consistently outperforms existing state-of-the-art methods and notably, even surpasses several supervised counterparts.
An Open-Source Dataset and A Multi-Task Model for Malay Named Entity Recognition

Named entity recognition (NER) is a fundamental task of natural language processing (NLP). However, most state-of-the-art research is mainly oriented to high-resource languages such as English and has not been widely applied to low-resource languages. In Malay language, relevant NER resources are limited. In this work, we propose a dataset construction framework, which is based on labeled datasets of homologous languages and iterative optimization, to build a Malay NER dataset (MYNER) comprising 28,991 sentences (over 384 thousand tokens). Additionally, to better integrate boundary information for NER, we propose a multi-task (MT) model with a bidirectional revision (Bi-revision) mechanism for Malay NER task. Specifically, an auxiliary task, boundary detection, is introduced to improve NER training in both explicit and implicit ways. Furthermore, a gated ignoring mechanism is proposed to conduct conditional label transfer and alleviate error propagation by the auxiliary task. Experimental results demonstrate that our model achieves comparable results over baselines on MYNER. The dataset and the model in this paper would be publicly released as a benchmark dataset.
Goods Stock Management using the K-Means Algorithm Method

The grouping of Mazaya products at PT. Bougenville Anugrah can still do manuals in calculating purchases, sales and product inventories. Requires time and data. For this reason, a research is needed to optimize the inventory of Mazaya goods by computerization. The method used in this research is K-Means Clustering on sales data of Mazaya products. The data processed is the purchase, sales and remaining inventory of Mazaya products in March to July 2019 totaling 40 pieces. Data is grouped into 3 clusters, namely cluster 0 for non-selling criteria, cluster 1 for best-selling criteria and cluster 2 for very best-selling criteria. The test results obtained are cluster 0 with 13 data, cluster 1 with 25 data and cluster 2 with 2 data. So to optimize inventory is to multiply goods in cluster 2, so as to save costs for management of Mazayaproducts that are not available. K-Means clustering method can be used for data processing using data mining in grouping data according to criteria.
The Effect of Using Mind Mapping in Comprehending Narrative Reading Text

Abstrak Tujuan utama dari penelitian ini adalah untuk mengetahui apakah Mind Map dapat menjadi media yang efektif untuk mengajar Bahasa Inggris dalam membaca teks naratif. Penelitian ini dilakukan di Sekolah Menengah Kejuruan Negeri 1 Sidoarjo. Subjek penelitian adalah 66 siswa Teknik Audio Video kelas 10 tahun akademik 2018/2019. Metode dalam penelitian ini adalah semi - Eksperimental. Penelitian dilakukan dalam dua kelas, mereka adalah kelas kontrol dan kelas eksperimental. Data dikumpulkan melalui tes yaitu pre-test dan post-test. Hasil penelitian menunjukkan bahwa teknik mind mapping efektif digunakan dalam pembelajaran membaca teks naratif. Skor yang diperoleh dari kelas eksperimen (9,55) lebih tinggi dari kelas kontrol (5,15). Dari hasil perhitungan statistik, diperoleh bahwa nilai t-observasi (to) adalah 1,852 dan derajat kebebasan (df) adalah 64. Dalam tabel signifikansi 5%, nilai derajat signifikansi adalah 1,669. Membandingkan nilai-nilai itu, hasilnya adalah 1,852> 1,669 yang berarti skor t-observasi (untuk) lebih tinggi dari skor t-tabel (tt). Dengan kata lain, Hipotesis Alternatif (Ha) diterima dan Hipotesis Null (Ho) ditolak. Oleh karena itu, mengajar pemahaman membaca teks naratif dengan menggunakan teknik pemetaan pikiran adalah efektif. Kata Kunci: Mengajar Reading, Teks Narrative, Mind Map Abstract The main objective of this study is to find out whether Mind Map can be effective as a medium for teaching English in reading narrative texts. This research was conducted at the Vocational High School of 1 Sidoarjo. The research subjects were 66 students of the 10th grade Audio Video Engineering academic year 201819. The method in this study is semi-experimental. The research was conducted in two classes, they were the control class and the experimental class. Data was collected through tests namely pre-test and post-test. The results showed that mind mapping techniques are effectively used in learning to read narrative texts. Scores obtained from the experimental class (9.55) are higher than the control class (5.15). From the results of statistical calculations, it is obtained that the value of t-observation (to) is 1.852 and the degree of freedom (df) is 64. In the 5% significance table, the value of the degree of significance is 1.669. Comparing the values, the result is 1.852> 1.669 which means the t-observation score (for) is higher than the t-table (tt) score. In other words, the Alternative Hypothesis (Ha) is accepted and the Null Hypothesis (Ho) is rejected. Therefore, teaching an understanding of narrative text reading using mind mapping techniques is effective. Key Terms: Teaching Reading, Narrative Text, Mind Map
AgreementLearning: An End-to-End Framework for Learning with Multiple Annotators without Groundtruth.

The annotation of domain experts is important for some medical applications where the objective groundtruth is ambiguous to define, e.g., the rehabilitation for some chronic diseases, and the prescreening of some musculoskeletal abnormalities without further medical examinations. However, improper uses of the annotations may hinder developing reliable models. On one hand, forcing the use of a single groundtruth generated from multiple annotations is less informative for the modeling. On the other hand, feeding the model with all the annotations without proper regularization is noisy given existing disagreements. For such issues, we propose a novel agreement learning framework to tackle the challenge of learning from multiple annotators without objective groundtruth. The framework has two streams, with one stream fitting with the multiple annotators and the other stream learning agreement information between the annotators. In particular, the agreement learning stream produces regularization information to the classifier stream, tuning its decision to be better in line with the agreement between the annotators. The proposed method can be easily plugged to existing backbones developed with majority-voted groundtruth or multiple annotations. Thereon, experiments on two medical datasets demonstrate improved agreement levels with annotators.
Discovery, Entropy, Organization, and Trust

This chapter discusses the dynamics of knowledge — specifically, how do ideas develop, and how does innovation take place? Here, the “cycle of discovery” that was presented earlier in Nooteboom1 is discussed: it is a theory of discovery at the individual and at the collective, organizational level. Can it be compared to the ancient cycle of yin and yang from Taoism? How does it relate to another principle that was proposed by the author, of the organization or communities within them as “focusing device?” Can the notion of “entropy” help in the analysis? Here, connectedness is added to entropy. Then, trust can be added to that.
Rafiki: Task-Level Capacity Planning in Distributed Stream Processing Systems

Distributed Stream Processing is a valuable paradigm for reliably processing vast amounts of data at high throughput rates with low end-to-end latencies. Most systems of this type offer a fine-grained level of control to parallelize the computation of individual tasks within a streaming job. Adjusting the parallelism of tasks has a direct impact on the overall level of throughput a job can provide as well as the amount of resources required to provide an adequate level of service. However, finding optimal parallelism configurations that fall within the expected Quality of Service requirements is no small feat to accomplish. In this paper we present Rafiki, an approach to automatically determine optimal parallelism configurations for Distributed Stream Processing jobs. Here we conduct a number of proactive profiling runs to gather information about the processing capacities of individual tasks, thereby making the selection of specific utilization targets possible. Understanding the capacity information enables users to adequately provision resources so that streaming jobs can deliver the desired level of service at a reduced operational cost with predictable recovery times. We implemented Rafiki prototypically together with Apache Flink where we demonstrate its usefulness experimentally.
Enhancing Encryption Algorithms through Parallelizing

Abstract: Encryption Algorithms are necessary part of the data sharing today as every bit of data need encryption so that it could be transferred from one place to another without the fear of data leaking. Every lost bit of data puts the system to danger. Encryption algorithms compose of necessary steps involving a key that helps converting plain text to cipher text. All the steps must be performed carefully to ensure that the data encrypted is found back. Thus, many algorithms are made to run sequentially and in serial. This makes the algorithm to take much computation time. Keywords: Parallelize, encryption algorithms, multi-threading.
Actividades matemáticas ocultas detrás de un mensaje de WhatsApp

We present an educational experience implemented with future teachers in mathematics through synchronous work through the Zoom platform. We analyze the use of technology students and focus on the mathematical activities involved. We conclude that digital technologies mediate the mathematical production of future teachers in such a way that their production would have been different without these technological elements. At the same time, we evidence the use of technologies and software as means to visualize and experiment and the putting into play of various activities characteristic of mathematical science.
Unfolding-Model-Based Visualization: Theory, Method and Applications

Multidimensional unfolding methods are widely used for visualizing item response data. Such methods project respondents and items simultaneously onto a low-dimensional Euclidian space, in which respondents and items are represented by ideal points, with person-person, item-item, and person-item similarities being captured by the Euclidian distances between the points. In this paper, we study the visualization of multidimensional unfolding from a statistical perspective. We cast multidimensional unfolding into an estimation problem, where the respondent and item ideal points are treated as parameters to be estimated. An estimator is then proposed for the simultaneous estimation of these parameters. Asymptotic theory is provided for the recovery of the ideal points, shedding lights on the validity of model-based visualization. An alternating projected gradient descent algorithm is proposed for the parameter estimation. We provide two illustrative examples, one on users' movie rating and the other on senate roll call voting.
ALGORITHMIZATION AND MOTION PROGRAMMING OF THE VACUUM CLEANER-ROBOT WITH PATH CORRECTION

The reliability of navigation in a dynamic or unknown environment depends on the ability of robot vacuum cleaners to move between unknown obstacles without collisions and rapid response to uncertainty. Problems associated with behavioral navigation systems are behavior coordination or choice of actions. Multiple behavior can simultaneously generate multiple command outputs, which can lead to the movement of the robot in unforeseen directions or to the complete failure of the system. Reliable system performance depends on deciding how to integrate high-level scheduling and low-level execution behavior, which behavior should be activated, and how the source commands should be combined into a single robot management command. The choice of fuzzy logic system is justified by the fact that it is ideal for navigation, as for a robot vacuum cleaner itself, and any other mobile robot that requires navigation in space: it is easy to use andimplement in software code. In essence, it resembles the use of the function IF… Then, but with much greater opportunities for the distribution and use of input values. This in turn allows for smooth adjustment of the output values, in our case – the angle of rotation, as well as speed. This research uses the CENTER OF GRAVITY (COG) method, which provides a clear value based on the center of gravity of a fuzzy set and is considered the most accurate of all defasification methods.This article presents the results of the development of an environment for simulating the movement of a robot vacuum cleaner, which allows one to estimate the differences between the applied algorithms for using fuzzy logic and the reliability of the chosen method. It is also possible to evaluate the effectiveness of the embedded logic for a path-choosing. In the simulation, it is possible to import a room layout, which gives an idea of the need to implement complex logic.
Data Mining Applied for Community Satisfaction Prediction of Rehabilitation and Reconstruction Project (Learn from Palu Disasters)

Natural disasters can occur anytime and anywhere, especially in areas with high disaster risk. The earthquake that followed the tsunami and liquefaction in Palu, Indonesia, at the end of 2018 had caused tremendous damage. In recent years, rehabilitation and reconstruction projects have been implemented to restore the situation and accelerate economic growth. A study is needed to determine whether the rehabilitation and reconstruction that has been carried out for three years have met community satisfaction. The results of further analysis are expected to predict the level of community satisfaction for the implementation of rehabilitation and other reconstruction. The method used in this paper is predictive modeling using a data mining (DM) approach. Data were collected from all rehabilitation and reconstruction activities in Palu, Sigi, and Donggala with the scope of the earthquake, tsunami, and liquefaction disasters. The analysis results show that the Artificial Neural Network (ANN) and the support vector machine (SVM) with a DM approach can develop a community satisfaction prediction model to implement rehabilitation and reconstruction after the earthquake-tsunami and liquefaction disasters.
Deep progressive attention for person re-identification

Person re-identification (Re-ID) aims to retrieve specific individuals across non-overlapping camera views. In recent years, attention-based models contribute to many computer vision tasks due to their great ability for learning discriminative features. We propose the deep progressive attention (DPA) in a more natural manner for person Re-ID. Similar to human visual attention mechanism, the proposed DPA progressively selects the most discriminative parts of a specific individual and formulates feature representation for comparison purpose. Concretely, on the one hand, the proposed DPA uses a long-term reward to optimize the discriminative feature selection. On the other hand, a deep convolutional architecture is integrated into a recurrent model for feature representation learning. Extensive experiments on three person Re-ID benchmarks Market-1501, DukeMTMC-reID, and CUHK03-NP demonstrate the proposed DPA is on par with the state-of-the-art. Moreover, the experiments on partial person Re-ID datasets indicate the proposed DPA is competitive with the specially designed partial person Re-ID methods.
Yet Meta Learning Can Adapt Fast, It Can Also Break Easily

Meta learning algorithms have been widely applied in many tasks for efficient learning, such as few-shot image classification and fast reinforcement learning. During meta training, the meta learner develops a common learning strategy, or experience, from a variety of learning tasks. Therefore, during meta test, the meta learner can use the learned strategy to quickly adapt to new tasks even with a few training samples. However, there is still a dark side about meta learning in terms of reliability and robustness. In particular, is meta learning vulnerable to adversarial attacks? In other words, would a well-trained meta learner utilize its learned experience to build wrong or likely useless knowledge, if an adversary unnoticeably manipulates the given training set? Without the understanding of this problem, it is extremely risky to apply meta learning in safety-critical applications. Thus, in this paper, we perform the initial study about adversarial attacks on meta learning under the few-shot classification problem. In particular, we formally define key elements of adversarial attacks unique to meta learning and propose the first attacking algorithm against meta learning under various settings. We evaluate the effectiveness of the proposed attacking strategy as well as the robustness of several representative meta learning algorithms. Experimental results demonstrate that the proposed attacking strategy can easily break the meta learner and meta learning is vulnerable to adversarial attacks. The implementation of the proposed framework will be released upon the acceptance of this paper.
Edge-Cloud Collaborated Object Detection via Difficult-Case Discriminator

As one of the basic tasks of computer vision, object detection has been widely used in many intelligent applications. However, object detection algorithms are usually heavyweight in computation, hindering their implementations on resource-constrained edge devices. Current edge-cloud collaboration methods, such as CNN partition over Edge-cloud devices, are not suitable for object detection since the huge data size of the intermediate results will introduce extravagant communication costs. To address this challenge, we propose a small-big model framework that deploys a big model in the cloud and a small model on the edge devices. Upon receiving data, the edge device operates a difficult-case discriminator to classify the images into easy cases and difficult cases according to the specific semantics of the images. The easy cases will be processed locally at the edge, and the difficult cases will be uploaded to the cloud. Experimental results on the VOC, COCO, HELMET datasets using two different object detection algorithms demonstrate that the small-big model system can detect 94.01%-97.84% of objects with only about 50% images uploaded to the cloud when using SSD. In addition, the small-big model averagely reaches 91.22%- 92.52% end-to-end mAP of the scheme that uploading all images to the cloud.
IMPROVING STUDENTS’ MOTIVATION THROUGH ENGLISH ONLINE LEARNING SPEAKING SKILLS

This study aims to discuss the efforts that can be made to increase students' motivation to learn English related to speaking skills in online learning during the pandemic. In this study, researchers are expected to provide information related to student motivation in carrying out learning related to speaking skills through online learning and the role of the teacher as a material deliverer who can provide more effective learning speaking material to students. By using the secondary data collection method, researcher uses related previous findings with research being written regarding increasing students' motivation in learning speaking skills during online learning. The specific purpose of this research is to help teachers to be able to find out what factors and media can help increase students' motivation in learning speaking skills. The findings of this study reveal that motivation becomes an important role in the process of students learning speaking skills through online learning, by using the right media students can pay more attention to the material presented. Keywords :  Online Learning, Motivation, Speaking Skills