Reviewerly/exHarmony huggingface dataset:
Tai chi action recognition based on structural LSTM with attention module

Tai chi is a traditional Chinese sport, which is popular all over the world. It is expressed by slow, soft, and continuously flowing moves. At present, studies on action recognition are generally aimed at common actions, such as walking, jumping. These algorithms are not very suitable for Tai chi recognition. Because Tai chi actions have unique characteristics. Through careful analysis of Tai chi moves and research of existing Tai chi dataset, we propose and build a Tai chi dataset, named Sub-Tai chi. This dataset is based on joints and skeleton, consisting of 15 representative basic actions of different body parts. For Tai chi action recognition, we use Structural LSTM with Attention Module, which is an action recognition method based on neural network. We use RNN to capture action features and use the full connected layer to classify actions. In this paper, we introduce the velocity features and acceleration features to improve Tai chi actions. Experimental results show that the method proposed in this paper has accuracy about 79%, which is nearly 7% higher than the original algorithm.
Prospects and challenges posed by blockchain technology on the copyright legal system

In the current copyright law and technology environment, two prominent problems must be considered when enforcing and executing copyrights: (i) the difficulty of proving authorship/ownership of a copyright; and (ii) the difficulty of conducting copyright transactions. The invention of blockchain technology has made it possible to solve these two issues. First, the use of blockchain technology with the InterPlanetary File System (IPFS) is an excellent mechanism for copyright authentication and document preservation. This mechanism allows authors (copyright owners) to prove authorship of their works and copyrights ownership. However, this mechanism for copyright authentication and document preservation may not be applicable in the future primarily because the computer software currently used may be upgraded in the following years. Second, the combination of Ethereum Blockchain and smart contracts can reduce transaction costs and enhance the efficiency of copyright assignments and licensing transactions. However, many legal problems regarding smart contracts have yet to be addressed. These problems include identifying the contracting parties in the anonymous blockchain network, dealing with scenarios where both contracting parties want to amend the implemented smart contracts, explaining code-based smart contracts and setting up an internal dispute resolution mechanism. Unlike the traditional transmission control protocol/Internet protocol network, where any activity is traceable, users on blockchain technology remain anonymous and their activities on the blockchain are hardly traceable. Although the blockchain itself is unlikely to become a hotbed of copyright infringement, the combination of the Ethereum Blockchain, the IPFS and smart contracts may make the entire process a hotbed of copyright infringement. Therefore, this situation poses considerable worries regarding copyright infringement on the blockchain. When the decentralized and anonymized blockchain and peripheral technologies mature, they will have considerable influence on copyright protection. This problem must be addressed by the copyright legal system in the current wave of blockchain technology implementation.
Application research of college English teaching resources based on knowledge base management platform

Abstract In this paper, based on the knowledge sharing in the college English teaching efficiency is low, and the existing network auxiliary teaching platform in the “information explosion” of potential crisis, with the demand of the teachers and students of high level of knowledge management, make full use of computer network technology, network database technology, on the basis of knowledge discovery technology. This paper constructs a platform model of college English teaching resource system based on knowledge base management platform, and expounds and analyzes the main function modules, implementation technologies, and attention matters in operation and management of this platform. This platform can provide rich knowledge base resources and convenient interactive communication, and improve the level of knowledge management in English teaching. Constantly improve the quality of teaching. Of great significance.
Integrated Model-Checking for the Design of Safe and Efficient Distributed Software Commissioning

We present MADA, a deployment approach to facilitate the design of efficient and safe distributed software commissioning. MADA is built on top of the Madeus formal model that focuses on the efficient execution of installation procedures. Madeus puts forward more parallelism than other commissioning models, which implies a greater complexity and a greater propensity for errors. MADA provides a new specific language on top of Madeus that allows the developer to easily define the properties that should be ensured during the commissioning process. Then, MADA automatically translates the description to a time Petri net and a set of TCTL formulae. MADA is evaluated on the OpenStack commissioning.
ASAP: Adaptive Structure Aware Pooling for Learning Hierarchical Graph Representations.

Graph Neural Networks (GNN) have been shown to work effectively for modeling graph structured data to solve tasks such as node classification, link prediction and graph classification. There has been some recent progress in defining the notion of pooling in graphs whereby the model tries to generate a graph level representation by downsampling and summarizing the information present in the nodes. Existing pooling methods either fail to effectively capture the graph substructure or do not easily scale to large graphs. In this work, we propose ASAP (Adaptive Structure Aware Pooling), a sparse and differentiable pooling method that addresses the limitations of previous graph pooling architectures. ASAP utilizes a novel self-attention network along with a modified GNN formulation to capture the importance of each node in a given graph. It also learns a sparse soft cluster assignment for nodes at each layer to effectively pool the subgraphs to form the pooled graph. Through extensive experiments on multiple datasets and theoretical analysis, we motivate our choice of the components used in ASAP. Our experimental results show that combining existing GNN architectures with ASAP leads to state-of-the-art results on multiple graph classification benchmarks. ASAP has an average improvement of 4%, compared to current sparse hierarchical state-of-the-art method.
A Projection-Based Method for Shape Measurement

This work addresses two main contributions for shape measurement: First, a new circularity measure for planar shapes is introduced based on their geometrical properties in the projection space of Radon transform. Second, a general-purpose evaluation criterion, power of discrimination, for assessing the efficiency of a shape measure is proposed. The new measure ranges over the interval [0, 1] and produces the value 1 if and only if the measured shape is a perfect circle. The proposed measure is invariant with respect to translation, rotation and scaling transformations. Moreover, it is also robust against border distortion of shapes. It is theoretically well founded and can be extended to other problems of shape measurement. Our approach can deal with complex shapes composed of connected components that cannot be handled by classical contour-based methods. Several experiments show its good behavior and demonstrate the efficiency and applicability of our proposed measure. Finally, we also consider our proposed evaluation criterion for assessing different circularity measures.
Detection and Localization of Video Object Removal by Spatio-Temporal LBP Coherence Analysis

Local object removal on video can directly affect our understanding and cognition of the video content without changing the motion continuity of other moving objects in the same video frame. Forgers can use video editing tools or certain inpainting techniques to remove undesired objects easily for covering up the truth. In this paper, we present a new approach based on spatio-temporal LBP coherence analysis for detection and localization of forged regions, which are generated by removing unwanted objects from the video. The proposed method starts with frames alignment to handle camera motion. And then the coherence analysis on the spatial LBP operator between two adjacent frames is performed to find the possible forged region. Finally, the temporal LBP operator is utilized to remove the false positives so as to obtain the final abnormal area. Two common region-level inpainting methods are adopted to simulate two different types of forgery processes for performance evaluation of our scheme. The experimental results prove that our method is effective in detecting and locating the forged regions and superior to the existing two approaches.
Towards identifying drug side effects from social media using active learning and crowd sourcing

Social media is a largely untapped source of information on side effects of drugs. Twitter in particular is widely used to report on everyday events and personal ailments. However, labeling this noisy data is a difficult problem because labeled training data is sparse and automatic labeling is error-prone. Crowd sourcing can help in such a scenario to obtain more reliable labels, but is expensive in comparison because workers have to be paid. To remedy this, semi-supervised active learning may reduce the number of labeled data needed and focus the manual labeling process on important information.We extracted data from Twitter using the public API. We subsequently use Amazon Mechanical Turk in combination with a state-of-the-art semi-supervised active learning method to label tweets with their associated drugs and side effects in two stages. Our results show that our method is an effective way of discovering side effects in tweets with an improvement from 53% F-measure to 67% F-measure as compared to a one stage work flow. Additionally, we show the effectiveness of the active learning scheme in reducing the labeling cost in comparison to a non-active baseline.Code and data will be published on https://github.com/kramerlab.
FuzzyGap: Sequential Pattern Mining for Predicting Chronic Heart Failure in Clinical Pathways.

The rapid growth of electronic health records (EHRs) facilitates the use of clinical pathways, an actionable plan for patients which is represented as sequences of diagnostic records ordered by visit dates. We propose to extract discriminative and representative clinical pathways from EHRs using sequential pattern mining. However, existing sequential patterns cannot efficiently extract patterns due to patient variations in length and time period between visits. To resolve this problem, we propose FuzzyGap, a sequential pattern mining-based framework that extracts a discriminative subsequent pattern from the proper representation of the sequence of encounters which also emphasizes the last visit that is more significant than others. We demonstrate FuzzyGap using a case study of heart failure and show the effectiveness of sequential pattern mining.
Comparison of Meta-heuristic Algorithms on Benchmark Functions

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&amp;amp;rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.
Discrete Stochastic Regulator on a Manifold, Minimizing Dispersion of the Output Macrovariable

A theoretical result is presented in the form of a new algorithm for the synthesis of a control system over a non-linear object, whose mathematical model represents a stochastic matrix difference equation having noise with a zero mean and finite dispersion in the righthand part. The new algorithm for synthesizing stochastic control for such an object is based on a three-stage procedure. In the first stage, the structure of the control system is formed in accordance with the classical method of analytical design of aggregated regulators (ADAR) in a fixed-noise assumption. In the second stage, the conditional mathematical expectation of the resulting expression for the first-stage control is determined. In the third stage, the control model is refined by excluding the noise variable from the control formula based on decomposing the initial control system affected by the new control. It is shown that the proposed control strategies minimize the target macro variable dispersion and ensure a stable, on average, achievement of the target manifold. A detailed example of an application of the algorithm for synthesizing control over the motion of an immobile center of mass is given, whose analog is represented by the objects such as by robot-manipulators, is given. The results of numerical modeling are presented, which confirm the operability of the constructed controller. Numerical simulations of the designed control system was performed using the authentic working equipment data.
The Impact of Social Media on EFL Learners’ Speaking Skill: A Survey Study Involving EFL Teachers and Students

Social media have gained much respect and popularity among EFL teachers who are in need to go beyond the traditional methods of teaching where teachers used to accomplish most parts of the job. Moreover, our traditional classrooms still emphasize on writing and reading at the expense of speaking. Therefore, teaching speaking represents a great challenge for both EFL teachers and learners who display low achievements in their oral skills. The current study aimed to investigate the effectiveness of using Social media on EFL students’ speaking skill. The research covers two basic issues. First, the extent to which Social media affect students’ speaking skill. Second, how can Social media be helpful in EFL context to improve speaking? The research was done at Islamic Azad Universities of Iran. Two questionnaires were distributed; one for 100 EFL teachers and the other one addressed to 100 learners. The practical outcome of the study has displayed that the frequent usage of Social media by EFL students as well as teachers constitutes a significant impact on both users. It has also reflected the teachers’ readiness to integrate Social media in EFL context by means of a convenient pedagogy. Technology is believed to make students respond positively in a language classroom, therefore, it is tremendously important for teachers to enrich their teaching process with the appropriate use of Social media and to promote a global understanding among the EFL Students who should broaden their perspectives about the target cultures and ameliorate their communicative competence in order to interact and communicate effectively and appropriately in English Language.
A Selective Modular Neural Network Framework

We propose a simple yet effective modular neural network framework for multi-class classification. The proposed framework significantly reduces the number of parameters while maintaining the accuracy comparable to more complex deep neural networks such as AlexNet and ResNet. The framework primarily consists of two major parts, namely a routing module and a set of expert modules. Each of the expert module is a binary classifier trained in Round Robin fashion. The novelty in this literature is on how we leverage the routing module to select only a small set of expert modules for each input datum during the testing phase. The selection of the expert module is carried out based on the routing module's soft-max scores for top-2 classes. In addition, each of the modules in the framework is very minimalist by design. The framework was evaluated on three real-world public data-sets. Empirical results show that with only a budget of 0.35M parameters we can achieve 99.56% accuracy on MNIST (0.2% improvement over AlexNet). For the Fashion-Mnist dataset, with the same budget and network architecture as MNIST we achieved 91.00% accuracy. For the UCI-HAR signal data, with a budget of only 2.5M, we can achieve an accuracy of 96.00% which is comparable to AlexNet (96.30%).
On the Construction of Multi-Relational Classifier Based on Canonical Correlation Analysis

In this paper, we introduce MRMDCCA for classifying multi relational data. Multi-relational data are stored on relational databases where they consist of multiple relations that are linked together by entity-relationship links. MRMDCCA takes advantage of correlation information of related relations to predict the class label. The proposed approach creates two different multiple feature sets, multiple feature sets based on propagating label information and multiple fused feature sets based on extracting correlation information. It propagates labels from the target table to the background tables based on foreign key paths to create multiple feature sets based on propagating label information. It proposes a approach based on Canonical correlation analysis (CCA) to extracting correlation information between related tables based on join paths to create multiple fused feature sets based on extracting correlation information. Finally, it applies traditional classifiers on two created feature sets and combines result of classifiers by using meta-learner. Testing has been performed on two diverse datasets. We compare our proposed classifier with other state-of-the-art multi relational classifiers which use different approaches to deal with multi relational setting. We showed that the proposed classifier achieves promising results in experiments.
Computational construction grammar for visual question answering

Abstract In order to be able to answer a natural language question, a computational system needs three main capabilities. First, the system needs to be able to analyze the question into a structured query, revealing its component parts and how these are combined. Second, it needs to have access to relevant knowledge sources, such as databases, texts or images. Third, it needs to be able to execute the query on these knowledge sources. This paper focuses on the first capability, presenting a novel approach to semantically parsing questions expressed in natural language. The method makes use of a computational construction grammar model for mapping questions onto their executable semantic representations. We demonstrate and evaluate the methodology on the CLEVR visual question answering benchmark task. Our system achieves a 100% accuracy, effectively solving the language understanding part of the benchmark task. Additionally, we demonstrate how this solution can be embedded in a full visual question answering system, in which a question is answered by executing its semantic representation on an image. The main advantages of the approach include (i) its transparent and interpretable properties, (ii) its extensibility, and (iii) the fact that the method does not rely on any annotated training data.
High‐Dimensional Quantum Communication: Benefits, Progress, and Future Challenges (Adv. Quantum Technol. 12/2019)

Quantum communication with states living in d-dimensional Hilbert spaces, qudits, yields great benefits. The main and clear advantage is that they can carry a larger amount of information per particle compared to 2D systems, qubits. The cover image figuratively shows this principle. However, qudits generation, transmission, and detection are not simple tasks to accomplish. Davide Bacco, Leif Katsuo Oxenløwe, and co-workers (article number 1900038) review the state-of-the-art on these topics, involving high-dimensional photonic quantum states, highlighting their advantages, issues, and future perspectives.
RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis

Understanding three-dimensional (3D) geometries from two-dimensional (2D) images without any labeled information is promising for understanding the real world without incurring annotation cost. We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images. The proposed method enables camera parameter conditional image generation and depth image generation without any 3D annotations such as camera poses or depth. We used an explicit 3D consistency loss for two RGBD images generated from different camera parameters in addition to the ordinal GAN objective. The loss is simple yet effective for any type of image generator such as the DCGAN and StyleGAN to be conditioned on camera parameters. We conducted experiments and demonstrated that the proposed method could learn 3D representations from 2D images with various generator architectures.
Latent Normalizing Flows for Many-to-Many Cross Domain Mappings

Learned joint representations of images and text form the backbone of several important cross-domain tasks such as image captioning. Prior work mostly maps both domains into a common latent representation in a purely supervised fashion. This is rather restrictive, however, as the two domains follow distinct generative processes. Therefore, we propose a novel semi-supervised framework, which models shared information between domains and domain-specific information separately. The information shared between the domains is aligned with an invertible neural network. Our model integrates normalising flow-based priors for the domain-specific information, which allows us to learn diverse many-to-many mappings between the two domains. We demonstrate the effectiveness of our model on diverse tasks, including image captioning and text-to-image synthesis.
METAPHYSICS AS A BASE FOR IMPROVING CRITICAL THINKING

Appears in: ICERI2019 Proceedings Publication year: 2019Pages: 8901-8903ISBN: 978-84-09-14755-7ISSN: 2340-1095doi: 10.21125/iceri.2019.2130Conference name: 12th annual International Conference of Education, Research and InnovationDates: 11-13 November, 2019Location: Seville, Spain
Augmented Reality As Android Based Learning Media for Wood Field Laboratory Work

Abstract The objectives of this research were: (1) producing Android-based AR products in the form of a Wood Field Laboratory Work module in Department of Civil and Planning Engineering Education, Faculty of Engineering UNY to facilitate students’ visualization abilities; (2) knowing the quality of Augmented Reality products for the Wood Field Laboratory Work module so that it is suitable for use by students and teaching staff; and (3) addressing the learning problems that have so far been present in the Wood Field Laboratory Work. The method used was the Software Development Life Cycle software development model which was described by the v-model waterfall. The v-model included: (1) the stage of specification of user requirements; (2) system architecture design; (3) designing components; (4) writing program codes; and (5) testing. The test results using ISO 25010 indicated that the application of learning media was stated to be very feasible in functional suitability, compability, and usability aspects. While from the performance efficiency aspect, the application was considered feasible because there was no memory leak on the device that run it, and CPU usage by five different devices reaches 10%, which is still below the safe limit (15%).