Reviewerly/exHarmony huggingface dataset:
Fuzzy Least Squares Support Vector Machine With Adaptive Membership for Object Tracking

Fuzzy learning has been introduced into tracking and achieved great success. However, the membership in the existing fuzzy learning based tracking algorithm is fixed, which lacks the adaptivity to measure the importance of the samples. To improve the tracking adaptivity and flexibility, in this paper, we propose a novel tracking method based on fuzzy least squares support vector machine with adaptive membership (FLS-SVM-AM). First, we formulate tracking as an adaptive membership based fuzzy learning problem, which addresses the issue of fixed membership in existing methods and can better measure the importance of the training samples. Second, we present the FLS-SVM-AM method to build the appearance model, and develop an iterative optimization process to solve the FLS-SVM-AM problem. Third, we define a new membership based on the PASCAL VOC overlap rate and exponential function, which is used to measure the importance of different samples more accurately. Experimental results in the benchmark datasets demonstrate that the proposed method not only outperforms the existing fuzzy learning based tracking methods, but also is comparable to many state-of-the-art methods.
A Face Recognition Algorithm Based on LBP-EHMM

In order to solve the problem that real-time face recognition is susceptible to illumination changes, this paper proposes a face recognition method that combines Local Binary Patterns (LBP) and Embedded Hidden Markov Model (EHMM). Face recognition method. The method firstly performs LBP preprocessing on the input face image, then extracts the feature vector, and finally sends the extracted feature observation vector to the EHMM for training or recognition. Experiments on multiple face databases show that the proposed algorithm is robust to illumination and improves recognition rate.
Penerapan Datamining pada Data Gempa Bumi Terhadap Potensi Tsunami di Indonesia

Indonesia is a maritime country that is located on 3 plates of the world or commonly called the Ring of Fire which causes frequent earthquakes. The earthquake is the biggest threat faced by the potential of the tsunami in it which can cause damage and even cause casualties. Data mining can explore pre-existing earthquake data and draw a pattern or conclusion from a database. The Naïve Bayes Classifier (NBC) algorithm is part of the data mining classification technique that is used to estimate or predict the chances of a possibility occurring. Based on the results and discussion, conclusions can be drawn by applying the Naïve Bayes Classifier (NBC) algorithm on earthquake data to potential tsunamis in Indonesia to find out the possible effects of earthquakes. With the testing data used the effect produced is the Potential Tsunami.
An Approach to Improve Technical Drawing using VR and AR Tools

As additive manufacturing (3D printing) technology becomes increasingly mainstream, new tools are required to enhance creative endeavors in disciplines such as fashion design, which have been slow to embrace digital technologies and computer-aided design software.Additionally, new techniques are required based on an understanding of the limitations of common desktop extrusion-based 3D printers, with 2.5D printing presenting new opportunities to rapidly produce complex forms, such as fashion, which can be assembled by hand as a hybrid approach to digital manufacturing.This paper presents a parametric system through which a new type of digitally fabricated, hand-assembled knit can be customized using a constrained selection of interactive controls.Novice users may rapidly iterate a pattern of loops and floats at a scale matching their Fused Deposition Modeling (FDM) 3D printer, creating a series of knit courses which can be assembled into a textile of expandable dimensions.More advanced users may modify the geometry or logic of the system, building new forms of knits that could not be manufactured through traditional means.This paper will guide designers through the process of developing a new typology of textile appropriate for production on ubiquitous FDM machines, following a workflow from 2D to 2.5D to 3D.Examples were printed at various scales and contribute to discourse on customization, parametric design and visual programming languages for additive manufacturing.
HISUI Status Toward 2020 Launch

Hyperspectral Imager Suite (HISUI) is a future spaceborne hyperspectral Earth imaging system being developed by Japanese Ministry of Economy, Trade, and Industry (METI). HISUI will be launched and deployed on International Space Station (ISS) for two - three year operation from 2020. Manufacturing and testing of Hyperspectral sensor were completed. Integration and test of HISUI Exposed Payload System is being conducted. HISUI data policy and research announcement as well as synergy with other earth observing instruments onboard ISS are also being discussed.
MVB: A Large-Scale Dataset for Baggage Re-Identification and Merged Siamese Networks

In this paper, we present a novel dataset named MVB (Multi View Baggage) for baggage ReID task which has some essential differences from person ReID. The features of MVB are three-fold. First, MVB is the first publicly released large-scale dataset that contains 4519 baggage identities and 22660 annotated baggage images as well as its surface material labels. Second, all baggage images are captured by specially-designed multi-view camera system to handle pose variation and occlusion, in order to obtain the 3D information of baggage surface as complete as possible. Third, MVB has remarkable inter-class similarity and intra-class dissimilarity, considering the fact that baggage might have very similar appearance while the data is collected in two real airport environments, where imaging factors varies significantly from each other. Moreover, we proposed a merged Siamese network as baseline model and evaluated its performance. Experiments and case study are conducted on MVB.
Different is Good

The use of uninitialized variables is a common issue. It could cause kernel information leak, which defeats the widely deployed security defense, i.e., kernel address space layout randomization (KASLR). Though a recent system called Bochspwn Reloaded reported multiple memory leaks in Windows kernels, how to effectively detect this issue is still largely behind.
How to (Easily) Extend the FAIRness of Existing Repositories

Data repository infrastructures for academics have appeared in waves since the dawn of Web technology. These waves are driven by changes in societal needs, archiving needs and the development of cloud computing resources. As such, the data repository landscape has many flavors when it comes to sustainability models, target audiences and feature sets. One thing that links all data repositories is a desire to make the content they host reusable, building on the core principles of cataloging content for economical and research speed efficiency. The FAIR principles are a common goal for all repository infrastructures to aim for. No matter what discipline or infrastructure, the goal of reusable content, for both humans and machines, is a common one. This is the first time that repositories can work toward a common goal that ultimately lends itself to interoperability. The idea that research can move further and faster as we un-silo these fantastic resources is an achievable one. This paper investigates the steps that existing repositories need to take in order to remain useful and relevant in a FAIR research world.
KnowSemLM: A Knowledge Infused Semantic Language Model

Story understanding requires developing expectations of what events come next in text.Prior knowledge -both statistical and declarative -is essential in guiding such expectations.While existing semantic language models (SemLM) capture event co-occurrence information by modeling event sequences as semantic frames, entities, and other semantic units, this paper aims at augmenting them with causal knowledge (i.e., one event is likely to lead to another).Such knowledge is modeled at the frame and entity level, and can be obtained either statistically from text or stated declaratively.The proposed method, KnowSemLM 1 , infuses this knowledge into a semantic LM by joint training and inference, and is shown to be effective on both the event cloze test and story/referent prediction tasks.
Enhancing Collaborative Filtering with Multi-label Classification

This paper presents a multi-label classification based CF framework, MLCF, which improves the quality of recommendation in the presence of data sparsity by learning over a heterogeneous information network consisting of a rating bipartite graph, a user graph and an item graph. MLCF is novel by three unique features. First, we explore the latent correlations among users and items w.r.t. a given set of K semantic categories beyond user-item ratings by employing multi-label clustering of items, and multi-label classification of users and rating-based similarities on the heterogeneous network. Second, based on the user/item/similarity multi-label clustering/classification, we propose a fine-grained multi-label classification based rating similarity measure to capture the class-specific relationships between users by introducing a novel concept of vertex-edge homophily. Third but not the least, we propose to integrate two kinds of multi-label classification based CF models focusing on rating and social information into a unified prediction model.
The possibilities of applying gamification technology in the implementation of e-learning at the university

Е. В. Карманова, А. Н. Старков, В. В
Applying Pedagogical Usability for Designing a Mobile Learning Application that Support Reading Comprehension

The pedagogical usability is an important characteristic of applications that support learning as it relates to the added value students perceive while using it for learning. A good pedagogical usability means that an application has more chances to be accepted and used by the students thus raising the possibilities that students actually will learn with it. However important, this concept tends to be neglected by many authors. In this work we show how this concept can be applied to evaluate an application by presenting a real example of an application that has been re-designed to improve its usability, thus showing how pedagogical usability can be operationalized to be applied in general. The application shown in this work is called RedCoMulApp (Reading Collaborative Multiple-option Application) and its goal is to raise the reading comprehension ability of 12th grade high school students. In order to capture the pedagogical usability, we used 12 metrics to design a questionnaire with 26 questions to be answered with a 5-point Likert-scale, plus two open-ended questions to capture aspects that were positively valued, and those that should be improved. The results for the pedagogical usability allow us to validate that the design of the application was perceived by the students as pedagogically useful to learning about reading comprehension.
3D Skeleton-Based Video Action Recognition by Graph Convolution Network

Human action recognition based on 3D skeleton data attracts more and more researchers because of its good robustness on the influence of light and occlusion. In previous studies, people only identified individual skeletons but ignored the fact that an action was usually performed by two or more people. Based on this idea, we trained a single and double person motion model based on 3D skeleton data and achieved a better recognition effect by analyzing the number of people involved in an action in the real-time recognition process. In response to these challenges, we propose a novel end-to-end model based on Graph Convolutional Network (GCN). Our model divides human skeleton into five regions (four limbs and trunk) to extract internal and inter-regional features. Experiments show that our model has reached the current advanced level on the NTU RGB-D dataset.
SUDA-Alibaba at MRP 2019: Graph-Based Models with BERT

In this paper, we describe our participating systems in the shared task on Cross-Framework Meaning Representation Parsing (MRP) at the 2019 Conference for Computational Language Learning (CoNLL).The task includes five frameworks for graph-based meaning representations, i.e., DM, PSD, EDS, UCCA, and AMR.One common characteristic of our systems is that we employ graph-based methods instead of transition-based methods when predicting edges between nodes.For SDP, we jointly perform edge prediction, frame tagging, and POS tagging via multi-task learning (MTL).For UCCA, we also jointly model a constituent tree parsing and a remote edge recovery task.For both EDS and AMR, we produce nodes first and edges second in a pipeline fashion.External resources like BERT are found helpful for all frameworks except AMR.
Discourse-Aware Hierarchical Attention Network for Extractive Single-Document Summarization

Discourse relations between sentences are often represented as a tree, and the tree structure provides important information for summarizers to create a short and coherent summary.However, current neural network-based summarizers treat the source document as just a sequence of sentences and ignore the tree-like discourse structure inherent in the document.To incorporate the information of a discourse tree structure into the neural network-based summarizers, we propose a discourse-aware neural extractive summarizer which can explicitly take into account the discourse dependency tree structure of the source document.Our discourseaware summarizer can jointly learn the discourse structure and the salience score of a sentence by using novel hierarchical attention modules, which can be trained on automatically parsed discourse dependency trees.Experimental results showed that our model achieved competitive or better performances against state-of-theart models in terms of ROUGE scores on the DailyMail dataset.We further conducted manual evaluations.The results showed that our approach also gained the coherence of the output summaries.
On the Computational Complexity of Multi-Agent Pathfinding on Directed Graphs

The determination of the computational complexity of multi-agent pathfinding on directed graphs has been an open problem for many years. For undirected graphs, solvability can be decided in polynomial time, as has been shown already in the eighties. Further, recently it has been shown that a special case on directed graphs can be decided in polynomial time. In this paper, we show that the problem is NP-hard in the general case. In addition, some upper bounds are proven.
SAR Image Simulation by Generative Adversarial Networks

SAR image simulation plays an important role in the process of SAR target interpretation and recognition, especially when the number of SAR images is limited. Due to the restriction of acquisition process, the numbers of SAR target images are always insufficient. The traditional SAR image simulation, which is based on calculation of electromagnetic theory, is easily to be affected by parameter distortion due to the lack of joint optimization. Consequently, it makes a big effect on the quality of the simulated images. This paper presents a novel approach, end-to-end models, to simulate the desired images from the SAR image database. A series of generative adversarial networks include DCGAN, weight clipping WGAN and WGAN with gradient penalty are optimized and applied to generate typical SAR target images. Three kinds of network structures are used, include structure of DCGAN, newly proposed structure of four residual blocks networks and Resnet. Experimental results show that the proposed novel method is not only efficient for SAR image simulation, but also can generate excellent SAR images. Furthermore, we analysis the results and the characteristics of different networks, which pave a good way for SAR image simulation based on artificial intelligence method.
Integrating Digital Technology in the School Curriculum

This article aims to challenge and interrogate the concept of integration of digital technology in the curriculum. We set the stage for the discussion taking the no-tion of curriculum and discussing the way teachers take it as a mediating tool and structuring resource for teaching. In the next step we engage in the discussion of learning as transformative participation in school social practices and locate digital technology within that problematic. We then go into the key claim of the article arguing for the myth of integration of digital technology in the curriculum. This provides the background for the formulation of a set of principles that would support the development of a framework for digital technology in education and contextual guidelines. To close we provide examples of topics under-represented in the research that are crucial to support such a framework.
Seeded self-play for language learning

How can we teach artificial agents to use human language flexibly to solve problems in real-world environments?We have an example of this in nature: human babies eventually learn to use human language to solve problems, and they are taught with an adult humanin-the-loop.Unfortunately, current machine learning methods (e.g. from deep reinforcement learning) are too data inefficient to learn language in this way.An outstanding goal is finding an algorithm with a suitable 'language learning prior' that allows it to learn human language, while minimizing the number of on-policy human interactions.In this paper, we propose to learn such a prior in simulation using an approach we call, Learning to Learn to Communicate (L2C).Specifically, in L2C we train a meta-learning agent in simulation to interact with populations of pre-trained agents, each with their own distinct communication protocol.Once the meta-learning agent is able to quickly adapt to each population of agents, it can be deployed in new populations, including populations speaking human language.Our key insight is that such populations can be obtained via self-play, after pre-training agents with imitation learning on a small amount of off-policy human language data.We call this latter technique Seeded Self-Play (S2P).Our preliminary experiments show that agents trained with L2C and S2P need fewer on-policy samples to learn a compositional language in a Lewis signaling game.
GO FAIR Brazil: A Challenge for Brazilian Data Science

The FAIR principles, an acronym for Findable, Accessible, Interoperable and Reusable, are recognised worldwide as key elements for good practice in all data management processes. To understand how the Brazilian scientific community is adhering to these principles, this article reports Brazilian adherence to the GO FAIR initiative through the creation of the GO FAIR Brazil Office and the manner in which they create their implementation networks. To contextualise this understanding, we provide a brief presentation of open data policies in Brazilian research and government, and finally, we describe a model that has been adopted for the GO FAIR Brazil implementation networks. The Brazilian Institute of Information in Science and Technology is responsible for the GO FAIR Brazil Office, which operates in all fields of knowledge and supports thematic implementation networks. Today, GO FAIR Brazil-Health is the first active implementation network in operation, which works in all health domains, serving as a model for other fields like agriculture, nuclear energy, and digital humanities, which are in the process of adherence negotiation. This report demonstrates the strong interest and effort from the Brazilian scientific communities in implementing the FAIR principles in their research data management practices.
BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification

We propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain.The model assumes that lexical cohesion can be observed across documents, meaning that segments describing the same topic use a similar lexical distribution over the vocabulary.The model implements lexical cohesion in an unsupervised Bayesian setting by drawing from the same language model segments with the same topic.Contrary to previous approaches, we assume that language models are not independent, since the vocabulary changes in consecutive segments are expected to be smooth and not abrupt.We achieve this by using a dynamic Dirichlet prior that takes into account data contributions from other topics.BeamSeg also models segment length properties of documents based on modality (textbooks, slides, etc.).The evaluation is carried out in three datasets.In two of them, improvements of up to 4.8% and 7.3% are obtained in the segmentation and topic identifications tasks, indicating that both tasks should be jointly modeled.
Feature Selection and Rule Generation Integrated Learning for Takagi-Sugeno-Kang Fuzzy System and its Application in Medical Data Classification

The rule-based fuzzy systems have successfully applied for numerous medical data classification problems. However, structuring the concise and interpretable fuzzy rules with good classification performance is still a big challenge. To address this issue, a novel feature selection and rule generation integrated learning for Takagi-Sugeno-Kang fuzzy system (called FSRG-IL-TSK) in this paper. FSRG-IL-TSK represents feature selection, structure identification and parameter learning into a Bayesian model, and uses the sequential importance resampling (SIR) algorithm to obtain the optimal parameters simultaneously, including the optimal features for each fuzzy rule, number of rules, and antecedent/consequent parameter of rules. Due to an integrated learning mechanism, it can select a small set of useful features and obtain a small number of rules. The effectiveness and advantages of FSRG-IL-TSK are validated experimentally on real-world medical data classification tasks.
Geometry teaching experience in virtual reality with NeoTrie VR

The possibilities of using the new software of virtual reality NeoTrie VR (or Neotrie) to teach geometry to children aged 11- 14 years old are presented. This software is tested for the first time as a part of math lessons at a School in Żernica (Poland). The use of Neotrie has made it easier to eliminate some of the problems faced by the pupils in the early stages of geometry learning. Moreover, the participating students solve geometric tasks more quickly, compared with previous years, especially those that require spatial imagination and those with a higher degree of difficulty. Since the program has been implemented, pupils are more active and prone to cooperation, and to formulate conclusions. Neotrie turns out to be a useful tool for teaching Geometry, it helps students to better organize their geometric knowledge in a ludic way, to increase their spatial reasoning and creativity.
Evaluation of shaft locations in underground mines: Fuzzy multi-objective optimization by ratio analysis with fuzzy cognitive map weights

The determination of the location of production and ventilation shafts is one of the most important issues in underground mines from both a technical and an economic viewpoint.This study introduces an integrated approach using fuzzy cognitive map (FCM) and fuzzy multi-objective optimization by ratio analysis (FMOORA) to evaluate several candidate shaft locations and consequently increase ore production from underground mines.The FCM based on a hybrid learning algorithm was applied to analyse interactions and internal relationships between criteria and to find complex causal relationships between different factors.After importing the weights resulting from the FCM into the FMOORA, shaft alternatives were evaluated and prioritized.An iron ore case example was evaluated by the integrated approach.Results of the integrated approach were validated by the results obtained from fuzzy-TOPSIS and on-site evaluations by mining experts.