Reviewerly/exHarmony huggingface dataset:
Learning Dynamic Coherence with Graph Attention Network for Biomedical Entity Linking

Biomedical entity linking, which aligns various disease mentions in unstructured documents to their corresponding standardized entities in a knowledge base (KB), is an essential task in biomedical natural language processing. Unlike in general domain, the specific challenge is that biomedical entities often have many variations in their surface forms, and there are limited biomedical corpora for learning the correspondence. Recently, biomedical entity linking has been shown to significantly benefit from neural-based deep learning approaches. However, existing works mostly have not exploited the topical coherence in their models. Moreover, most of the collective models use a sequence-based approach, which may generate an accumulation of errors and perform unnecessary computation over irrelevant entities. Most importantly, these models ignore the relationships among mentions within a single document, which are very useful for linking the entities. In this paper, we propose an effective graph attention neural network, which can dynamically capture the relationships between entity mentions and learn the coherence representation. Besides, unlike graph-based models in general domain, our model does not require large extra resources to learn representations. We conduct extensive experiments on two biomedical datasets. The results show that our model achieves promising results.
Video stabilization method corresponding to various imagery for geostationary optical Earth observation satellite

In geostationary Earth observation satellite system with high-resolution optical system under conceptual study phase developed by Japan Aerospace Exploration Agency (JAXA), one of its main products for users is a video with one fps captured by the telescope and the optical sensor; however, there have been few examples of satellite video with high-rate fps, especially in geostationary satellite. A satellite video has instability of the video sequences caused by an undesired disturbance of the satellite. In this study, we propose the video stabilization method corresponding to various satellite imagery characteristics, using feature point matching and bundle adjustment with eliminating outliers by using RANdom SAmple Consensus (RANSAC). Moreover, we adopt a polynomial approximation to smooth global motions of all video frames. We demonstrate the video stabilization using the experimental video captured by the helicopter. Moreover, the performance test result shows that the proposed method can stabilize the satellite video less than one pixel accuracy. In addition, we discuss various issues specific to a satellite imagery, for example, moving cloud.
DSNet: Dynamic Selection Network for Biomedical Image Segmentation

This paper focuses on uterine segmentation, an important clue for understanding MRI images and medical analysis of expectant mothers, which has long been underestimated. Related works have proven that the receptive field is crucial in computer vision. However, current methods usually use pooling operations to continuously enlarge the receptive field, which leads to some inevitable information loss. In this paper, we design the Dynamic Selection Module (DSM) to effectively capture the spatial perception of medical images. Specifically, DSM adopts dynamic convolution kernel to adaptively adjust the receptive field in the horizontal and vertical directions. We then combine DSM and residual block to construct a Dynamic Residual Unit (DRU) which further learns feature representation. Then DRU is embedded in the standard U-Net termed Dynamic Selection Network (DSNet). We evaluate our method on the Uterus dataset we acquired. To validate the generalization of this method, we also do the same experiment on the Gland Segmentation dataset and Lung dataset. The results demonstrate that DSNet can significantly boost the performance of medical image segmentation than other related encoder-decoder architectures.
How saccadic vision might help with the interpretability of deep networks

We describe how some problems (interpretability, lack of object-orientedness) of modern deep networks potentially could be solved by adapting a biologically plausible saccadic mechanism of perception. A sketch of such a saccadic vision model is proposed. Proof of concept experimental results are provided to support the proposed approach.
BGADAM: Boosting based Genetic-Evolutionary ADAM for Neural Network Optimization

For various optimization methods, gradient descent-based algorithms can achieve outstanding performance and have been widely used in various tasks. Among those commonly used algorithms, ADAM owns many advantages such as fast convergence with both the momentum term and the adaptive learning rate. However, since the loss functions of most deep neural networks are non-convex, ADAM also shares the drawback of getting stuck in local optima easily. To resolve such a problem, the idea of combining genetic algorithm with base learners is introduced to rediscover the best solutions. Nonetheless, from our analysis, the idea of combining genetic algorithm with a batch of base learners still has its shortcomings. The effectiveness of genetic algorithm can hardly be guaranteed if the unit models converge to close or the same solutions. To resolve this problem and further maximize the advantages of genetic algorithm with base learners, we propose to implement the boosting strategy for input model training, which can subsequently improve the effectiveness of genetic algorithm. In this paper, we introduce a novel optimization algorithm, namely Boosting based Genetic ADAM (BGADAM). With both theoretic analysis and empirical experiments, we will show that adding the boosting strategy into the BGADAM model can help models jump out the local optima and converge to better solutions.
Expression recognition algorithm based on local and global feature reconstruction residuals

In this paper, an algorithm is proposed to improve expression recognition by combining reconstructed residuals of local and global information. Firstly, positional relation is introduced into local binary features encoding, and a non-negative matrix decomposition method with sparse correlation constraints is employed to perform reducing of dimension. Then, the features are encoded by bag-of-words model to form local features. Secondly, a discrete wavelet transform is used to emphasis on expression information and extract expression-enhanced global features. Thirdly, collaboration Represents is used to calculate local partial reconstruction residuals and is combined with global reconstruction residuals to construct the final residual to be classified at the decision level. Experiments on Jaffe, TEFID and CK+ data sets prove the effectiveness of the proposed algorithm.
Hubungan Pengelolaan Kelas terhadap Hasil Belajar Siswa

The purpose of this study was to determine the relationship between classroom management and students' learning outcomes at grade IV SDN 010 Tembilahan Hulu. This type of research was quantitative research. The data in this study were collected using questionnaires, documentation and analyzed using product-moment correlation with the help of SPSS 22.0. The results of the data analysis showed that the value of sig. (2-tailed) between classroom management (x) and learning outcomes (y) was 0.000 &lt;0.05, which means that there was a significant correlation between classroom management variables and learning outcomes. The calculated r-value for the relationship between classroom management (x) and learning outcomes (y) was 0.642 &gt; from 0.374, so it can conclude that there was a relationship or correlation between classroom management and learning outcomes. Because the calculated r in this analysis was positive, meaning that the relationship between the two variables was positive.
Improving Visual Question Answering by Semantic Segmentation

Most recent visual question answering (VQA) methods extract object regions (bounding-boxes) by Faster R-CNN and use these region features in the visual encoder. Because extracted bounding-boxes are often located around things (countable objects), information on stuff (amorphous background regions such as grass and sky) is not reflected well in the visual encoder. Because stuff is amorphous and uncountable, it is common to use semantic segmentation to extract its features. In this work, we extend conventional thing-centric regions-of-interest (ROIs) by adding ROIs distributed around stuff regions and use semantic segmentation labels to encode stuff features in the visual encoder. The results of our experiments revealed that our method improved on existing VQA models and produced state-of-the-art results on VQA-v2 val, even though this dataset was not designed specifically for evaluating stuff, and most of its questions are thing-centric.
Supply Chain Management System Design in Vendor Selection Process

This research aimed to create and built a system on choosing a vendor to deliver a Delivery Order on that day in PT Mayora Indah Tbk. The design of this program uses the methodology of EXtreme programming which the steps are, Planning, Design, Coding, Testing. The analysis is done on the process of choosing the vendor’s business that is working on that time. Then, based on the result of the analysis that is being included on the new design of business process and also is one a direct interview to the users that needs to support on complying the user requirement that is needed. By designing this program, so that can make the logistic easier to choose the vendor that will be use to send Delivery Order and also gives a discretion on every vendor to participate on the bidding process.
Scrum, Sampling, and the 90 Percent Syndrome

Relying upon a long-standing prediction derived from one of Software Engineering’s most durable and respected software development models, we show that the primary methodological design decision shaping Scrum can be understood as a sampling-based solution to the 90% Syndrome -- a solution that exhibits exactly the characteristics predicted for such a solution. Aside from contributing credence to Scrum’s claim of improved predictability, this direct connection to the "normal science" software engineering of the 80’s and 90’s refutes the widely held assumption that Scrum (along with other Agile methods) represents a major paradigm shift or a "Kuhnian revolution" and thus requires some new "theory of agility". Instead, we argue, that Scrum should be viewed as solving the 90% Syndrome in a manner that affirms normal science, and that its sampling strategy constitutes the central methodological design choice, independently providing coherent justification for nearly every other Scrum-specific methodological design choice. We close with observations about the implications for future research and about the limitations of this work.
Research on the Strategy of Smart Classroom Construction Based on Teachers’ Data Literacy

The rapid development of quality education and the proposal of the theme of reducing educational burden under the new situation have further promoted the pace of curriculum teaching reform. Exploring the development strategy ofa hybrid teaching model based on knowledge graphs with quality education as the core has important educational practical significance. In the digital age, smart classroom is the inevitable trend of the development of digital technology. Teachers’ data literacy reflects the requirements of intelligent classroom for teachers. This paper discusses the connotation of smart classroom and the construction strategy of smart classroom based on Teachers’ data literacy, which helps to improve teachers’ data literacy, build smart classroom, promote the in-depth integration of teachers’ data literacy and smart classroom construction, and provide reference for the development of teachers’ data literacy and the teaching practice of smart classroom.
Effects of Using Two Dimensional Multiple Transform Domains, Mutual Principal and Minor Component Analysis on Face Recognition

A face recognition system based on Five Transform Domains – Two Dimensional – Principal and Minor Component Analysis; TDs – 2D – Mu(P+m)CA is proposed. Each face in the spatial domain is first preprocessed then divided into horizontal, vertical halves and diagonal format. Second, these partitions are compressed using the Discrete Wavelet Transform (DWT). Third they are further transformed using the Discrete Cosine Transform (DCT). In addition, starting from the DWT, each partition underwent three processes, namely Horizontal and Vertical Edge Detection and High pass Filtered. Third, each partition is further compressed using 2DPCA. Finally, after computing and saving best principal components, minor components are investigated if they can further boost the recognition rate. A voting scheme is used to define ground truth identity. The performance of the proposed system is evaluated using k-fold cross validation of ORL, Yale and FERET databases. Sample results are presented. Compared with the state of art techniques, the proposed technique achieves higher recognition rates while retaining low savings in storage and computation requirements.
A Data-oriented Approach for Detecting offensive Language in Arabic Tweets

The growing popularity of social media (SM) platforms has made these platforms a crucial part of modern societies. Users from different cultures, backgrounds, demographics get aboard in an increasing manner to express their views, stances, and opinions on a varied range of topics. Since users on SM can easily hide their real identity, a closer look at daily posts on social medial platforms shows that users do not seem to reflect only their stances and views, but also, they get an opportunity for revealing their behaviors, which could be negative towards the others. Although only a small population of SM users can show negative behavior towards other individuals, groups, and society in general, the impact could be catastrophic. This has resulted in the emerge of terms like cyberbullying, online extremism/hatred/threatening, online trolling, online political-polarity discourse. To ensure safe social networking, the domain of automatic detection of offensive/hatred language has lately grown notably. This work focuses on utilizing a publicly available dataset of Arabic tweets labeled for offensive/non-offensive language. Unlike previous work which focuses merely on developing and tuning machine learning models to be as accurate as possible on the benchmark dataset used, we turn to focus on the characteristics of the offensive language used in SM. The purpose is to have an in-depth look into the dataset to disclose what seems to be hidden patterns in offensive language expressed daily online. Our findings reveal the benefit of using larger training dataset that covers a wide range of offensive language patterns to build robust machine learning classifiers with a better ability to generalize well on highly sparse data used in SM.
Comprehensive Multi-Agent Epistemic Planning

Over the last few years, the concept of Artificial Intelligence has become central in different tasks concerning both our daily life and several working scenarios. Among these tasks automated planning has always been central in the AI research community. In particular, this manuscript is focused on a specialized kind of planning known as Multi-agent Epistemic Planning (MEP). Epistemic Planning (EP) refers to an automated planning setting where the agent reasons in the space of knowledge/beliefs states and tries to find a plan to reach a desirable state from a starting one. Its general form, the MEP problem, involves multiple agents who need to reason about both the state of the world and the information flows between agents. To tackle the MEP problem several tools have been developed and, while the diversity of approaches has led to a deeper understanding of the problem space, each proposed tool lacks some abilities and does not allow for a comprehensive investigation of the information flows. That is why, the objective of our work is to formalize an environment where a complete characterization of the agents' knowledge/beliefs interaction and update is possible. In particular, we aim to achieve such goal by defining a new action-based language for multi-agent epistemic planning and to implement an epistemic planner based on it. This solver should provide a tool flexible enough to reason on different domains, e.g., economy, security, justice and politics, where considering others' knowledge/beliefs could lead to winning strategies.
On the Use of FCA Models in Static Analysis Tools to Detect Common Errors in Programming

Static code analysis is widely used to detect code quality issues before execution. Thus, it can provide information that is important for improving programming skills. Such tools have been successfully used in teaching programming courses. In this paper we present algorithms for combining Formal Concept Analysis (FCA) with Pylint, a static code analysis tool, in order to detect and assess behavioral patterns in students’ programming styles. We design a generalized framework that can be subsequently used to analyze any category/subset of errors. We apply the approach for detecting common errors related to design by considering two scenarios that tackle object oriented design and increased code complexity. We argue how the results can be used to understand common mistakes and to improve the teaching content and methods.
Assessing Speaker Interpolation in Neural Text-to-Speech

This paper presents a study on voice interpolation in the framework of neural text-to-speech. Two main approaches are considered. The first one consists of adding three independent speaker embeddings at 3 different positions within the model. The second one substitutes the embedding vectors by convolutional layers, kernels of which are computed on the fly from reference spectrograms. The interpolation between speakers is done by linear interpolation between the speaker embeddings in the first case, and between convolution kernels in the second. Finally, we propose a new method for evaluating interpolation smoothness using agreements between interpolation weights, objective and subjective speaker similarities. The results indicate that both methods are able to produce smooth interpolation to some extent, with the one based on learned speaker embeddings yielding better results.
X-Bridge: Image-to-Image Translation with Reconstruction Capabilities

This work presents a novel method for image-to-image translation named X-Bridge. The method is based on a conditional adversarial network. X-Bridge is a supervised method build upon the Pix2pix approach, however, it extends the original system with an additional reconstruction path and a shared-latent space assumption between the original and the reconstruction path. With these modifications, we argue that the qualitative results provided by X-Bridge overcome other state-of-the-art methods in terms of similarity between translated and corresponding images, robustness, generalization capacity, and translated features preservation. This assumption is confirmed with provided quantitative results. We demonstrate the power of this approach on the challenging facial image-to-sketch translation task. Code is available at: https://github.com/YvanG/Cross-modal-Bridge.
Adaptive Graph Laplacian for Convex Multi-Task Learning SVM

Multi-Task Learning (MTL) aims at solving different tasks simultaneously to obtain better models. Some Support Vector Machines (SVMs) formulations for the MTL context involve the combination of common and task-independent models, where we can also use an homogeneous graph over the tasks to impose pairwise connections between the independent models. The graph weight matrix is usually taken as a uniform (i.e., maximum entropy) one that connects equally every pair of tasks. If we take instead the identity matrix (i.e., the one of minimum entropy), then no connections are considered. In MTL situations where the real tasks or the relationships among them are unknown, these two rigid approaches can be detrimental to the learning process. In order to identify the true underlying tasks and their structure, we propose here a Convex Adaptive Graph Laplacian MTL SVM, where the task models and the graph that reflects their relationships are learned in a sequential and collaborative way. We illustrate on four different synthetic scenarios the task structure identification capacities of this approach and how it can lead to better models.
Comparison of modified Karnik-Mendel algorithm-based interval type-2 ANFIS and type-1 ANFIS

Purpose This study aims to propose, as the first time, the interval type-2 adaptive network-fuzzy inference system (ANFIS) structure, which is given better results compared to previously presented in the open literature. So, the ANFIS can be used effectively for training of interval type-2 fuzzy logic system (IT2FLS) parameters. Design/methodology/approach Karnik–Mendel algorithm (KMA) is modified to use in interval type-2 ANFIS. The modified Karnik–Mendel algorithm (M-KMA) is implemented to change the uncertain ANFIS parameters into known ones. In this way, the interval type-2 ANFIS removes uncertainties of IT2FLS. Therefore, the interval type-2 ANFIS is reduced to a simple one, i.e. less mathematical operation required. Only consequent parameters are trained, and the consequent parameters are chosen in the form of crisp. Findings By applying the mentioned procedure, it can be shown that interval type-2 ANFIS has generally better results compared to type-1 ANFIS. However, it was noticed that the worst results obtained in the case of interval type-2 ANFIS are equal to the best result obtained in the case of type-1 ANFIS. Therefore, users in this field can use this approach in solving nonlinear problems. Practical implications The interval type-2 ANFIS can be used as controller for highly nonlinear systems such as air vehicles. Originality/value As stated in the open literature, it is ineffective to use ANFIS for IT2FLS. In this study, the KMA is modified for IT2FLS, and it is seen that the ANFIS can be used effectively for IT2FLS.
Hourglass Face Detector for Hard Face

Face detection is an upstream task of facial image analysis. In many real-world scenarios, we need to detect small, occluded or dense faces that are hard to detect, but hard face detection is a challenging task in particular considering the balance between accuracy and inference speed for real-world applications. This paper proposes an Hourglass Face Detector (HFD) for hard face by developing a deep one-stage fully-convolutional hourglass network, which achieves an excellent balance between accuracy and inference speed. To this end, the HFD firstly shrinks a feature map by a series of stridden convolutional layers rather than pooling layers, so that useful subtle information is preserved better. Secondly, it exploits context information by merging fine-grained shallow feature maps with deep ones full of semantic information, making a better fusion of detailed information and semantic information to achieve a better detection of small faces. Moreover, the HFD exploits prior and multiscale information from the training data to enhance its scale-invariance and adaptability of anchor scales. Compared with the SSH and S3FD methods, the HFD can achieve a better performance in average precision on detecting hard faces as well as a quicker inference. Experiments on the WIDER FACE and FDDB datasets demonstrate the superior performance of our proposed method.