Reviewerly/exHarmony huggingface dataset:
Semi-Supervised learning with Collaborative Bagged Multi-label K-Nearest-Neighbors

Abstract Over the last few years, Multi-label classification has received significant attention from researchers to solve many issues in many fields. The manual annotation of available datasets is time-consuming and need a huge effort from the expert, especially for Multi-label applications in which each example of learning is associated with many labels at once. To overcome the manual annotation drawback, and to take advantages from the large amounts of unlabeled data, many semi-supervised approaches were proposed in the literature to give more sophisticated and fast solutions to support the automatic labeling of the unlabeled data. In this paper, a Collaborative Bagged Multi-label K-Nearest-Neighbors ( CobMLKNN ) algorithm is proposed, that extend the co-Training paradigm by a Multi-label K-Nearest-Neighbors algorithm. Experiments on ten real-world Multi-label datasets show the effectiveness of CobMLKNN algorithm to improve the performance of MLKNN to learn from a small number of labeled samples by exploiting unlabeled samples.
Implementation of Push Notification for Business Incubator

Push notification of cellular is one of the important features in delivery of information from a mobile application. The push notification feature is provided by firebase cloud messaging in sending notification messages to android devices. The application of push notification on the network for business incubator tenant selection can help improve the process and quality of information distribution in building communication networks and information for startups. This activity is one of the business incubators activities in looking for startups potention and technology products that are deserve to be selected as potential tenants and partners.
FLIPPED CLASSROOM TECHNOLOGY AND ITS IMPLEMENTATION AT VARIOUS LEVELS OF HIGHER EDUCATION

The fourth industrial revolution influences the system of education requiring abilities and competencies that go beyond what has traditionally been taught in the classroom. The main concern is the social nature of learning and the ability to collaborate using digital technologies. It also results in some general shifts in the world of learning – lifelong learning – from childhood, to continuous learning in the workplace. Foreign language as an academic subject has a great potential to meet the needs of industry 4.0 as it is taught at all levels of education. There are a lot of modern technologies of teaching languages: teaching in cooperation, project method, modular and blended learning. In this article we will focus on the Flipped classroom technology at different levels of education: bachelor, master, postgraduate training. The advantages and limitations of using this method at different levels are revealed. We provide an overview of information technologies that can be used to implement this method. Guidelines are given on how to ensure the technological continuity of the educational process, using the Flipped classroom, to develop students ' readiness for different ways of knowledge transfer. The authors describe the flipped class model and experience of using the flipped classroom technology teaching English at bachelor's, master's and advanced training courses at the South Ural State University.
Amelioration of Teaching Strategies by Exploring Code Quality and Submission Behavior

Online learning platforms provide an opportunity to better understand students’ weaknesses by tracking both their learning behavior and knowledge. This information can then be used to assist teachers in making instructional decisions and to further guide those who are at risk of failure. In this paper, we tracked student learning data from a C++ programming course over a whole semester of their freshman year via the Trustie platform. A total of 17,854 code submissions were collected. We then used CppCheck, SonarQube and Trustie to capture the quality characteristics and submission characteristics of the code, including lineOfCode, cyclomaticComplexity, codeSmell, syntacticError, averageScore, submission, and logicError, and analyzed the impact of code quality on the assignment work results. Several factors were discovered that we believe can help teachers to develop more effective teaching strategies.
A Stochastic Virtual Machine Placement Algorithm for Energy-Efficient Cyber-Physical Cloud Systems

With the integration of cyber-physical system and cloud computing, virtual machine (VM) placement has been of great importance to the performance of cyber-physical cloud system (CPCS). This paper proposes a stochastic VM placement algorithm that takes into account the uncertainty of resource requirements while minimizing the total energy consumption in a CPCS. Different from existing approaches that use deterministic values to represent the resource demands, the proposed stochastic VM placement algorithm models the uncertainty of resource requirements as random variables, and further formulates the uncertainty-affected VM placement problem as a stochastic optimization model. The optimization objective is to minimize the total energy consumed by all servers under the constraint of a user-specified overflow probability (i.e., the probability of demanded resources exceeding the capacity of the server). To solve the formulated stochastic optimization problem, we further propose an efficient metaheuristic algorithm to search for an energy-efficient VM placement solution that can tolerate resource requirement variations. Experimental results demonstrate that, compared with traditional deterministic VM placement algorithms, the stochastic method considering uncertain resource requirements can achieve more energy-efficient placement solutions while providing guaranteed service level.
Prediction of interaction intention based on eye movement gaze feature

In this paper, an interactive intention prediction method based on eye movement gaze features of typical interaction paradigm of user interface was proposed. Firstly, the common graphical user interface (GUI) was abstracted, and five typical interaction paradigms, Tab, Form, List, Tree and Filter were obtained. According to the characteristics of information organization structure of five typical interaction paradigms, five experimental task interfaces and tasks were designed, and eye movement gaze characteristics were recorded by eye tracker during the experiment. Cluster analysis was conducted on the subjects' fixation coordinates under different experimental interface conditions, and the spatial distribution characteristics of fixation were obtained. The clustering analysis results were used as the input of support vector machine (SVM), and the typical interaction paradigm types which subject were gazed are predicted. The 64% accuracy rate is obtained, which verifies the effectiveness of the proposed method.
Modular service provision for heterogeneous patient groups: a single case study in chronic Down syndrome care

Service modularity could be promising for organizing healthcare delivery to heterogeneous patient groups because it enables cost reductions while also being responsive towards individual patients' needs. However, no research on the applicability of modularity in this context exists. To this end, we conducted a qualitative single-case study on chronic healthcare provision for Down syndrome patients, delivered by multidisciplinary pediatric Downteams in the Netherlands, from a modular perspective.We conducted six semi-structured interviews with coordinators of multidisciplinary Downteams in six hospitals. In addition, we gathered data by means of observations and analysis of relevant documentation. We transcribed, coded, and analyzed the interviews utilizing the Miles and Huberman approach. The consolidated criteria for reporting qualitative research (COREQ) were applied in this study.In all six Downteams studied, the modular package for Down syndrome patients (i.e. the visit to the Downteams) could clearly be divided into modules (i.e. the separate consultations with the various professionals), and into different components (i.e. sub-elements of these consultations). These modules and components were linked by different types of customer-flow and information-flow interfaces. These interfaces allowed patients to flow smoothly through the system and allowed for information transfer, respectively.Our study shows a modular perspective is applicable to analyzing chronic healthcare for a heterogeneous patient group like children with Down syndrome. The decomposition of the various Downteams into modules and components led to mutual insight into each other's professional practices, both within and across the various Downteams studied. It could be used to increase transparency of delivered care for patients and family. Moreover, it could be used to customize care provision by mixing-and-matching components. More detailed research on chronic modular care provision for patients with DS is needed to explore this.
A Compiler for Deep Neural Network Accelerators to Generate Optimized Code for a Wide Range of Data Parameters from a Hand-crafted Computation Kernel

This paper presents the design and implementation of a compiler for a deep neural network accelerator that provides high performance and energy efficiency. The compiler allows deep learning frameworks, such as TensorFlow, to exploit the accelerator hardware by automatically creating data transfer code and outer loops around highly-tuned hand-crafted inner-loops for a wide range of neural network parameters. In other words, our compiler significantly reduces the development effort for deep learning libraries without sacrificing their performance. We have evaluated our prototype compiler to show that it can generate code for five most-critical deep learning operators with a comparative performance obtained from hand-tuned code.
Fork Path: Batching ORAM Requests to Remove Redundant Memory Accesses

Outsourcing data to a third-party cloud provider has become quite common with the increasing use of cloud computing. This brings convenience, as well as the concern for data security and privacy. It is believed that data encryption alone is often not enough to protect users' privacy from the cloud provider. According to previous work, the sequence of storage locations accessed by the client can leak up to 90% of the sensitive information, even with data encrypted. In this context, Oblivious RAM (ORAM) is proposed. ORAM algorithms allow the client to hide its access pattern from the service provider while introducing a lot of extra operations. Among all the prototypes, Path ORAM is one of the most promising designs. However, there are still redundant memory accesses that can be removed without harming the security of traditional ORAM as we observed. We came up with three optimization techniques, including path merging, ORAM request scheduling, and merging aware caching. We also propose a prefetching technique to further decreasing the access overhead. Moreover, we also illustrate the compatibility of Fork Path and some state-of-the-art Path ORAM optimizations. Compared to traditional Path ORAM approaches, our Fork Path ORAM can reduce overall performance overhead and power consumption of memory system by 65% and 44%, while the design overhead is trivial.
Teachers’ Self-efficacy and Performance in Teaching Literature in the Interest-based Classes at Senior High School

Teacher’s self-efficacy is a specific phenomenon that can be seen as one of the contributors to the process of learning and effective teaching. Teacher self-efficacy was investigated at two Senior High Schools (SMAN 1 and SMAN 2) in Kendari, and proved its importance in the interest-based classes at senior high schools in Kendari. Findings revealed two major methods employed in the research study: 1) interviews conducted in the form of an open interview with 5 teachers; and 2) observation was focused on teachers teaching materials, classroom management, as well as the enthusiasm of the teacher during the learning process takes place. Based on the finding, this study argues that teachers have high confidence in their ability to teach literature because they ascribe high value to their performance. However, the self-efficacy of teachers did not have an impact when applied in practice. In the classroom observation, these elements generally did not show up. This shows that the dependence of teachers on textbooks is not accompanied by an increase in the ability of teachers to ask questions to the students during the learning process, so that the high self-efficacy of teachers does not produce learning achievement is high. This goal can be achieved by giving more opportunities and experiences for teachers to improve the quality of learning.
Grafted network for person re-identification

Convolutional neural networks have shown outstanding effectiveness in person re-identification (re-ID). However, the models always have large number of parameters and much computation for mobile application. In order to relieve this problem, we propose a novel grafted network (GraftedNet), which is designed by grafting a high-accuracy rootstock and a light-weighted scion. The rootstock is based on the former parts of ResNet-50 to provide a strong baseline, while the scion is a new designed module, composed of the latter parts of SqueezeNet, to compress the parameters. To extract more discriminative feature representation, a joint multi-level and part-based feature is proposed. In addition, to train GraftedNet efficiently, we propose an accompanying learning method, by adding an accompanying branch to train the model in training and removing it in testing for saving parameters and computation. On three public person re-ID benchmarks (Market1501, DukeMTMC-reID and CUHK03), the effectiveness of GraftedNet is evaluated and its components are analyzed. Experimental results show that the proposed GraftedNet achieves 93.02%, 85.3% and 76.2% in Rank-1 and 81.6%, 74.7% and 71.6% in mAP, with only 4.6M parameters.
A Novel Deep Multi-Modal Feature Fusion Method for Celebrity Video Identification

In this paper, we develop a novel multi-modal feature fusion method for the 2019 iQIYI Celebrity Video Identification Challenge, which is held in conjunction with ACM MM 2019. The purpose of this challenge is to retrieve all the video clips of a given identity in the testing set. In this challenge, the multi-modal features of a celebrity are encouraged to be combined for a promising performance, such as face features, head features, body features, and audio features. As we know, the features from different modalities usually have their own influences on the results. To achieve better results, a novel weighted multi-modal feature fusion method is designed to obtain the final feature representation. After many experimental verification, we found that different feature fusion weights for training and testing make the method robust to multi-modal person identification. Experiments on the iQIYI-VID-2019 dataset show that our multi-modal feature fusion strategy effectively improves the accuracy of person identification. Specifically, for competition, we use a single model to get the result of 0.8952 in mAP, which ranks TOP-5 among all the competitive results.
Paralysis Proofs

The growing adoption of digital assets---including but not limited to cryptocurrencies, tokens, and even identities---calls for secure and robust digital assets custody. A common way to distribute the ownership of a digital asset is (M, N)-threshold access structures. However, traditional access structures leave users with a painful choice. Setting M = N seems attractive as it offers maximum resistance to share compromise, but it also causes maximum brittleness: A single lost share renders the asset permanently frozen, inducing paralysis. Lowering M improves availability, but degrades security.
Innovative unscented transform–based particle cardinalized probability hypothesis density filter for multi-target tracking

Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.
Customizing a Feature Ontology for Product Line Engineering within a System-of-Systems Context

System of systems (SoS) is an arrangement of systems that results when independent systems are integrated into a larger system that delivers unique capabilities that cannot be provided by the constituent systems. Product line engineering (PLE) is a large scale comprehensive reuse approach to reduce the cost of development, reduce the time-to-market, and increase the quality of the system. In this paper, we report on our experiences in developing a PLE approach for a SoS context, that is SoS-PLE. The results are derived from an action research within the context of Aselsan, a large scale systems engineering company. One of the challenges for SoS-PLE is the selection and customization of the required ontology for commonality and variability modeling for the SoS context. Various different feature modeling approaches have been provided in the literature, but these have largely focused on single PLE. We show the analysis and selection of a feature ontology for SoS-PLE. Subsequently, the selected feature ontology is enhanced and integrated within the product line engineering process. The results of the paper provide novel insight for the state-of-the-practice in PLE for systems engineering, and elaborates on and complement the existing literature on feature ontologies and SoSs.
Perception-Based CTU Level Bit Allocation for Intra High Efficiency Video Coding

The conventional rate control algorithm does not fully consider the characteristics of the Human Visual System (HVS). To address this problem, in this paper, a perception based Coding Tree Unit (CTU) level bit allocation algorithm for intra High Efficiency Video Coding (HEVC) is proposed, which aims to minimize the perceptual distortion of each CTU under a given bit rate constraint. Firstly, to better represent perceptual distortion, Perceptually Weighted Mean Squared Error (PWMSE) is adopted instead of Mean Squared Error (MSE). Then, the relationship between Rate ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R)$ </tex-math></inline-formula> and Perceptually Weighted Distortion ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$D_{p})$ </tex-math></inline-formula> is formulated by the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R-</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{D}_{p}$ </tex-math></inline-formula> model. Finally, the perceptual weighting factor derived from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R-</i> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\textit{D}_{p}$ </tex-math></inline-formula> model is used to guide CTU level bit allocation. Experimental results show that the proposed algorithm achieves 6.27% bit rate reduction and 0.38 dB Bjøntegaard Delta Perceptually Weighted Peak Signal to Noise Ratio (BD-PWPSNR) gain on average. Under other seven quality evaluation metrics, the proposed algorithm achieves from 1.57% to 8.95% average bit rate reduction while maintaining the perceptual quality, which significantly outperforms the benchmark schemes. Moreover, the proposed perception-based CTU level bit allocation algorithm also maintains a high rate control accuracy, which reaches 99.997%.
3D Human Pose Estimation in Vietnamese Traditional Martial Art Videos

Preserving, maintaining and teaching traditional martial arts are very important activities in social life. That helps preserve national culture, exercise and self-defense for practitioners. However, traditional martial arts have many different postures and activities of the body and body parts are diverse. The problem of estimating the actions of the human body still has many challenges, such as accuracy, obscurity, etc. In this paper, we survey several strong studies in the recent years for 3-D human pose estimation. Statistical tables have been compiled for years, typical results of these studies on the Human 3.6m dataset have been summarized. We also present a comparative study for 3-D human pose estimation based on the method that uses a single image. This study based on the methods that use the Convolutional Neural Network (CNN) for 2-D pose estimation, and then using 3-D pose library for mapping the 2-D results into the 3-D space. The CNNs model is trained on the benchmark datasets as MSCOCO Keypoints Challenge dataset [1], Human 3.6m [2], MPII dataset [3], LSP [4], [5], etc. We final publish the dataset of Vietnamese's traditional martial arts in Binh Dinh province for evaluating the 3-D human pose estimation. Quantitative results are presented and evaluated.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium provided the original work is properly cited.
Knowledge of uncertain worlds: programming with logical constraints

Abstract Programming with logic for sophisticated applications must deal with recursion and negation, which together have created significant challenges in logic, leading to many different, conflicting semantics of rules. This paper describes a unified language, DA logic, for design and analysis logic, based on the unifying founded semantics and constraint semantics, that supports the power and ease of programming with different intended semantics. The key idea is to provide meta-constraints, support the use of uncertain information in the form of either undefined values or possible combinations of values and promote the use of knowledge units that can be instantiated by any new predicates, including predicates with additional arguments.