Reviewerly/exHarmony huggingface dataset:
User Preference Translation Model for Recommendation System with Item Influence Diffusion Embedding

Recommendation systems which are designed to understand and predict user interest based on user preferences play an important role in the era of information explosion. We propose the item influence embedding which adopts the social influence diffusion concept to model the item relations. We can learn the activation paths in items-item relation graph. In addition, for generating top-k items, most of recommendation systems calculate the similarity between user embedding and embedding of all items. The calculation costs too much time when number of users and items are huge. Therefore, we propose the User Preference Translation Model (UPTM) to recommend the Top-k items based on the language translation technology. UPTM directly generates the recommendation items based on translating the user preference. We can avoid to calculate the similarity of user embedding and item embedding. From the experimental results, UPTM not only outperforms the compared methods but also save the time in real large datasets.
Efficient Image Retrieval Based on Support Vector Machine and Genetic Algorithm Using Color, Texture and Shape Features

Content based image retrieval (CBIR) systems can find similar images to a query image in a large image database. This technique is based on the visual features of the image. In this work we propose a CBIR system based on the three descriptors of the image which are the color, texture and shape features. This study extracts robust features from all dataset images and the query image with the same manner. The image descriptors are extracted from the color histogram, gray level co-occurrence matrix and the Hu moments. Then, a classification technique based on the support vector machine is applied to the features database to create four image classes in the purpose of reducing the query time and limiting the search interval. Meanwhile, the image retrieval is performed based on an efficient meta-heuristic algorithm which is the genetic algorithm. The precision and recall measurements are computed based on the obtained results to validate the efficiency of our CBIR system.
Segmentation and Recognition of Offline Sketch Scenes Using Dynamic Programming

Sketch recognition aims to segment and identify objects in a collection of hand-drawn strokes. In general, segmentation is a computationally demanding process since it requires searching through a large number of possible recognition hypotheses. It has been shown that, if the drawing order of the strokes is known, as in the case of online drawing, a class of efficient recognition algorithms becomes applicable. In this article, we introduce a method that achieves efficient segmentation and recognition in offline drawings by combining dynamic programming with a novel stroke ordering method. Through rigorous evaluation, we demonstrate that the combined system is efficient as promised, and either beats or matches the state of the art in well-established databases and benchmarks.
A new scheme of automatic modulation classification using Convolutional Neural Network with constellation diagram

Automatic Modulation Classification (AMC) of the transmitted signals remains a challenging area in modern intelligent communication systems such as cognitive radio system. In this research, we propose a new system for AMC using Convolutional Neural Network (CNN) with constellation diagrams in Additive White Gaussian Noise channel and use the trained model to recognite five digital modulation methods: BPSK, QPSK, 8PSK, 16QAM and 64QAM with constellation diagrams. We have achieved high efficiency and high precision recognition of modulation methods by using new CNN models, improved input image size, number of dropout layers and simulation hyperparameters.
Trident: Change Point Detection for Multivariate Time Series via Dual-Level Attention Learning

Change point detection is an important subset of anomaly detection problems. Due to the ever-increasing volume of time-series data, detecting change points has important significance, which can find anomalies early and reduce losses, yet very challenging as it is affected by periodicity, multi-input series, and long time series. The performance of traditional methods typically scales poorly. In this paper, we propose Trident, a novel prediction-based change point detection approach via dual-level attention learning. As the name implies, our model consists of three key modules which are the prediction, detection, and selection module. The three modules are integrated in a principled way of detecting change points more accurately and efficiently. Simulations and experiments highlight the effectiveness and efficacy of the Trident for change point detection in time series. Our approach outperforms the state-of-the-art methods on two real-world datasets.
The development of environmental knowledge and the use of solar energy using environmental responsibility behavior in Central Java

Abstract Energy has an essential meaning in life to continue to develop and support economic and environmental activities. Every year energy in Indonesia increases rapidly in line with human growth. To supply energy needs, renewable energy sources must be developed. Renewable energy potentials such as solar energy have never been used on a large scale even though Indonesia has enormous energy potential. Solar energy can be used in the form of solar cells for electricity generation. The utilization of solar cells can help people who live in remote areas far from the electricity network. This research was conducted in Central Java by using descriptive qualitative methods and using research and development (R&amp;D) methods. The technique used is an expressive qualitative analysis technique. The result solution implemented is to meet the increasing electrical energy needs of the community. This research produces renewable energy that can be used as a solution to solving the electrical energy crisis at an affordable cost and can increase the independence of the community so that it does not depend on government electricity supply and improve environmental responsibility behavior.
Neural Demographic Prediction in Social Media with Deep Multi-view Multi-task Learning

Utilizing the demographic information of social media users is very essential for personalized online services. However, it is difficult to collect such information in most realistic scenarios. Luckily, the reviews posted by users can provide rich clues for inferring their demographics, since users with different demographics such as gender and age usually have differences in their contents and expressing styles. In this paper, we propose a neural approach for demographic prediction based on user reviews. The core of our approach is a deep multi-view multi-task learning model. Our model first learns context representations from reviews using a context encoder, which takes semantics and syntactics into consideration. Meanwhile, we learn sentiment and topic representations from selected sentiment and topic words using a word encoder separately, which consists of a convolutional neural network to capture the local contexts of reviews in word-level. Then, we learn a unified user representation from context, sentiment and topic representations and apply multi-task learning for inferring user's gender and age simultaneously. Experimental results on three real-world datasets validate the effectiveness of our approach. To facilitate future research, we release the codes and datasets at https://github.com/icmpnorequest/DASFAA2021_DMVMT .
Worldbuilding from tectonic first principles: Integrating and challenging undergraduate knowledge through a course project

In this integrative, team-based capstone project, students in an upper year Tectonics course built a planet of their own design from the basic concepts of plate tectonics. They created a tectonic map complete with plate boundaries, continental-ocean boundaries, motion vectors, and hot spot traces. In subsequent weeks, students produced a geologic map illustrating features that were a consequence of decisions made in creating their tectonic map. Students designed their own legend and determined the specificity or generality in which they conveyed geologic features at the scale of their map. Students also created a physiographic map reflective of the geology of their planet. Evaluation of student artifacts indicates the project was successful in meeting learning outcomes that challenged students to think in three-dimensions and over geologic timescales, and to develop problem-solving skills; a learning outcome of integrating knowledge from other courses was also met, but to a lesser degree and with more variability among teams. With minor adjustments, this project could be implemented in any tectonics course, or in other subjects including oceanography, atmospheric science, or paleontology.
An Argument Extraction Decoder in Open Information Extraction

In this paper, we present a feature fusion decoder for argument extraction in Open Information Extraction (Open IE), where we challenge argument extraction as a predicate-dependent task. Therefore, we create a predicate-specific embedding layer to allow the argument extraction module fully shares the predicate information and the contextualized information of the given sentence, after using a pre-trained BERT model to achieve the predicates. After that, we propose a decoder in argument extraction that leverages both token features and span features to extract arguments with two steps as argument boundary identification by token features and argument role labeling by span features. Experimental results show that the proposed decoder significantly enhances the extraction performance. Our approach establishes a new state-of-the-art result on two benchmarks as OIE2016 and Re-OIE2016.
A First Look at COVID-19 Domain Names: Origin and Implications

This work takes a first look at domain names related to COVID-19 (Cov19doms in short), using a large-scale registered Internet domain name database, which accounts for 260 M of distinct domain names registered for 1.6 K of distinct top-level domains. We extracted 167 K of Cov19doms that have been registered between the end of December 2019 and the end of September 2020. We attempt to answer the following research questions through our measurement study: RQ1: Is the number of Cov19doms registrations correlated with the COVID-19 outbreaks?, RQ2: For what purpose do people register Cov19doms? Our chief findings are as follows: (1) Similar to the global COVID-19 pandemic observed around April 2020, the number of Cov19doms registrations also experienced the drastic growth, which, interestingly, pre-ceded the COVID-19 pandemic by about a month, (2) 70% of active Cov19doms websites with visible content provided useful information such as health, tools, or product sales related to COVID-19, and (3) non-negligible number of registered Cov19doms was used for malicious purposes. These findings imply that it has become more challenging to distinguish domain names registered for legitimate purposes from others and that it is crucial to pay close attention to how Cov19doms will be used/misused in the future.
Learning Rule-Based Explanatory Models from Exploratory Multi-Simulation for Decision-Support Under Uncertainty

Exploratory modeling and simulation is an effective strategy when there are substantial contextual uncertainty and representational ambiguity in problem formulation. However, two significant challenges impede the use of an ensemble of models in exploratory simulation. The first challenge involves streamlining the maintenance and synthesis of multiple models from plausible features that are identified from and subject to the constraints of the research hypothesis. The second challenge is making sense of the data generated by multi-simulation over a model ensemble. To address both challenges, we introduce a computational framework that integrates feature-driven variability management with an anticipatory learning classifier system to generate explanatory rules from multi-simulation data.
Image decomposition-based sparse extreme pixel-level feature detection model with application to medical images

Pixel-level feature detection from images is an essential but challenging task encountered in domains such as detecting defects in manufacturing systems and detecting tumors in medical imaging. Often, the real image contains multiple feature types. The types with higher pixel intensities are termed as positive (extreme) features and the ones with lower pixel intensities as negative (extreme) features. For example, when planning a medical treatment, it is important to identify, (a) calcification (a pathological feature which can result in a post-surgical complications) as positive features, and (b) soft tissues (organ morphology, knowledge of which can support pre-surgical planning) as negative features, from a preoperative computed tomography image of the human heart. However, this is not an easy task because (a) conventional segmentation techniques require manual intervention and post-processing, and (b) existing automatic approaches do not distinguish positive features from negative. In this work, we propose a novel, automatic image decomposition-based sparse extreme pixel-level feature detection model to decompose an image into mean and extreme features. To estimate model parameters, a high-dimensional least squares regression with regularization and constraints is utilized. An efficient algorithm based on the alternating direction method of multipliers and the proximal gradient method is developed to solve the large-scale optimization problem. The effectiveness of the proposed model is demonstrated using synthetic tests and a real-world case study, where the model exhibits superior performance over existing methods.
Multi-person pose tracking with occlusion solving using motion models

We present a method for the multi-person human tracking problem including occlusion solving. To track and associate frame-by-frame human detections obtained using a deep learning approach, we propose to combine motion features extracted by optical flow and Kalman filtering, which allow us to predict the future poses of targets. By taking advantage of the characteristics of both motions features, we are able to handle sharp motions of the target and occlusions. With our simple occlusion handling mechanism, we achieve comparable results with state of the art and are able to keep track of a target identity even when occlusions occur.
Unbiased Gradient Simulation for Zeroth-Order Optimization

We apply the Multi-Level Monte Carlo technique to get an unbiased estimator for the gradient of an optimization function. This procedure requires four exact or noisy function evaluations and produces an unbiased estimator for the gradient at one point. We apply this estimator to a non-convex stochastic programming problem. Under mild assumptions, our algorithm achieves a complexity bound independent of the dimension, compared with the typical one that grows linearly with the dimension.
Joint Feature Representation and Classifier Learning Based Unsupervised Domain Adaption ELM

In the problem of unsupervised domain adaption Extreme learning machine (ELM), the output layer parameters need to have both classification and domain adaptation functions, which often cannot be simultaneously fully utilized. In addition, traditional matching method based on data probability distribution cannot find the common subspace of source and target domains under large difference between domains. In order to alleviate the pressure of double functions of classifier parameters, the entire ELM learning process is mainly divided into two stages: feature representation and adaptive classifier learning, thus a joint feature representation and classifier learning based unsupervised domain adaption ELM model is proposed. In the feature representation stage, the source and target domain data are projected to their respective subspace while minimizing the difference in probability distribution between the two domains. In the adaptive classifier learning stage, the smooth manifold regularization term of target domain is used to improve the parameter adaptive ability. Experiments on six different types of datasets show that the proposed model has higher cross-domain classification accuracy.
A literature review: Augmented reality-based learning in Sundanese Siger Bridal Make-up

Abstract Augmented reality is a technology that is being developed and aimed at advancing and facilitating education. Technological progress, encouraging technology-based learning media needs to be studied first theoretically, before being applied to the learning process. This research is a literacy study, which sees augmented reality as supporting the Sundanese Siger Bridal Makeup course. The purpose of this research is to maximize the use of technology in the learning process and to see the effectiveness of augmented reality, especially in vocational learning. Also, this research is intended to analyse the extent to which technology is important as support in learning. The results of the study show that (a) Technology today is the main key in teaching and learning activities, (b) Augmented reality, is considered attractive so that it can increase the focus of students during the learning process, (c) selection of appropriate educational technology, in cosmetology Sundanese Siger bride, able to increase student understanding of the material being taught.
MACROBERT: Maximizing Certified Region of BERT to Adversarial Word Substitutions

Deep neural networks are deemed to be powerful but vulnerable, because they will be easily fooled by carefully-crafted adversarial examples. Therefore, it is of great importance to develop models with certified robustness, which can provably guarantee that the prediction will not be easily misled by any possible attack. Recently, although a certified method based on randomized smoothing is proposed, it does not take the maximized certified region into account, so we develop an approach to train models with maximized certified regions via replacing the base classifier with the soft smoothed classifier which is differentiable during propagation.
Behavioral Cloning from Observation with Bi-directional Dynamics Model

Robotics is rapidly expanding its workplace from industrial factories to more complicated fields to work on behalf of human. The difficulty of programming the operations human does in advance, however, prevents this expansion. Behavioral cloning is one of the promising approaches to acquire the operations effectively from the expert's demonstrations, which consist of the states and the performed actions of the expert. However, it is intractable and/or highly expensive for robots to measure the expert's actions. Behavioral cloning from observation fills this gap and makes it possible to imitate with state-only demonstrations by inferring actions that the expert performed from an inverse dynamics model. Our goal is to improve the accuracy of this algorithm. This is done by evaluating the inferred action using an additional forward dynamics model. Specifically, we focus on the consistency in both dynamics models, which have to be bi-directional. This bi-directionality, can classify whether the inferred action is realistic or not, and can prevent wrong updates. We show the successful improvement with our new method using various simulation tasks which are typically used in benchmarks.
Cloud Computing based Monolithic to Containerization using Elastic Container Service for Phylogenetic Analysis

Tremendous growth in Biotechnology field and the newer inventions have posed a lot of challenges to the technology domain which replicates the Demand-Supply model. The Phylogenetic analysis process involves analysing the sequences and building various forms of trees which depicts the evolutionary relationship among the organisms of interest. This process involves functioning of pipeline of tools related to Phylogenetics, various methods and models are in use which uses monolithic architecture which is a traditional unified model method. The monolithic model building has a cognitive complexity code base and scalability is a major drawback, which can be addressed by application containerization technology. Containerization is an application virtualization technology implemented at Operating System level. In this approach distributed applications can be deployed and executed by sharing resources of virtual or physical machine(s) with other containerised applications. This paper proposes a containerization model against the existing monolithic model specific to Phylogenetics. Further it also gives insight on deploying containerization applications on public cloud (AWS) using Elastic Container Service.
DEAM: Adaptive Momentum with Discriminative Weight for Stochastic Optimization

Optimization algorithms with momentum, e.g., (ADAM) helps accelerate SGD in parameter updating, which can minify the oscillations of parameters update route. However, the fixed momentum weight (e.g., β <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> in ADAM) will propagate errors in momentum computing. Besides, such a hyperparameter can be extremely hard to tune in applications. In this paper, we introduce a novel optimization algorithm, namely Discriminative wEight on Adaptive Momentum (DEAM). DEAM proposes to compute the momentum weight automatically based on the discriminative angle. The momentum term weight will be assigned with an appropriate value which configures the influence of momentum in the current step. In addition, DEAM also contains a novel backtrack term, which restricts redundant updates when the correction of the last step is needed. The backtrack term can effectively adapt the learning rate and achieve the anticipatory update as well. Extensive experiments demonstrate that DEAM can achieve a faster convergence rate than the existing optimization algorithms in training various models. A full version of this paper can be accessed in [1].
A Case Study in the Banking Sector: An Ontology for the Selection of Agile and Lean Software Development Methodologies

Nowadays, the application of methodologies that allow to guide the process of development of Software in the companies has become a fundamental aspect to achieve the suitable management of the processes in the projects. In view of the diversity of existing methodologies, there is a growing interest in defining strategies that allow the selection and application of the correct methodology, which adjusts to the characteristics of the work teams and the software projects. The aim of this work is to develop an ontology for the selection of the methodology that, according to its principles, is most appropriate and beneficial for the development of software projects. The domain of ontology is limited to the Agile and Lean approaches, without defining for Agile the specific method that it applies, but it involves any method that is governed by the agile values and principles established in the Agile manifesto. Onto-logy is applied in two organizations in the banking sector, allowing recommendations to be inferred for the use of Agile methodology in both, which will make it possible to reduce the delivery time of software products, improve communication between project participants, and facilitate the engineering of requirements. On the other hand, the ontology suggests co-regulating characteristic aspects of the Lean practices in order to minimize costs, optimize processes in the software projects and improve the organizational culture.