Reviewerly/exHarmony huggingface dataset:
Three-Dimensional Image Analysis of the Movement Characteristics of Nunchakus Kick Technique

Standard taekwondo turning kick technique motions are very important resources in teaching, training and further research in the future. In this paper, it is mainly the research and design on inherent regularity of taekwondo turning kick technique motion. Firstly, a turning kick technology motion image collection plan based on three-dimensional image is proposed and the collected motion images are processed to get kinematics parameters (velocity, displacement, etc.) of motion. Then testing apparatus of combating event group is utilized to directly obtain kinetic parameters (force, torque, momentum, etc). Finally, through MATLAB these parameters are processed secondly, thus obtaining the changing rules of parameters in turning kick technology motion. The results show that the scientific and rational muscle force order has ensured good hitting effect, the intensity and range of upper limb swing related to the waist and hips also affects the hitting effect, meanwhile the strength, speed and explosive force have significant effect on hitting effect.
Technical Details and Educational Applications for Virtual Reality Technologies

The application of virtual reality (VR) in higher education has drawn attention. Understanding the state of the art for VR technologies helps educators identify appropriate applications and develop a high-quality engaging teaching-learning process. This chapter provides a comprehensive survey of current hardware and software supports on VR. Secondly, important technical metrics in VR technology are considered with comparisons of different VR devices using identified metrics. Third, there is a focus on software tools and an explore of various development frameworks, which facilitate the implementation of VR applications. With this information as a foundation, there is a VR use in higher education. Finally, there is a discussion of VR applications that can be potentially used in education.
Scale-Aware Segmentation of Multiple-Scale Objects in Aerial Images

Semantic segmentation is a fundamental task extensively used in the analysis of high-resolution aerial images. Due to the objects with various scales widely appearing in high-resolution aerial images, the fixed square receptive field in most existing DCNNs cannot work well and usually leads to unexpected predictions. To alleviate this problem, we propose a light-weight scale-aware module (SWD), which is end-to-end differentiable and can be embedded in most existing networks. In our module, we employ the re-sampling scheme to make each element of convolutional patches adjust its position, which explicitly adjusts the receptive field size to fit different scales of target objects. Further, considering re-sampled feature maps as weighted maps implicitly introduces spatial attention mechanism. As a result, with only 0. 2M additional parameters, the network embedded with our module can automatically adjust its receptive field to better recognize objects in various scales. In the experiment, we evaluate our method on the ISPRS Vaihingen Dataset, especially the analysis of buildings category, which usually in different scales. We further compare it to the spatial attention mechanism and mainstream networks that utilize multi-scale information. The experimental results and comprehensive analysis demonstrate the effectiveness and efficiency of our proposed method.
Dealing with Critical Issues in Emails: A Comparison of Approaches for Sentiment Analysis

The customer service of larger companies is constantly faced with the challenge of mastering the daily flood of incoming emails. In particular, the effort involved in dealing with critical issues, such as complaints, and the insufficient resources available to deal with them can have a negative impact on customer relations and thus on the public perception of companies. It is therefore necessary to assess and prioritise these concerns automatically, if possible. It is therefore necessary to evaluate and prioritise these concerns automatically if possible. The sentiment analysis as the automatic recognition of the sentiment in texts enables such prioritisation. The sentiment analysis of German-language e-mails is still an open research problem and till now there is no evidence of a dominant approach in this field. The aim of this article is to compare three approaches for the sentiment analysis of German emails: The first approach (A) is based on the combination of sentiment lexicons and machine learning methods. The second (B) is the extension of approach A by further feature extraction methods and the third approach (C) is a deep learning approach based on the combination of Word Embeddings and Convolutional Neural Networks (CNN). A gold standard corpus is generated to compare these approaches. Based on this corpus, systematic experiments are carried out in which the different method combinations for the approaches are examined. The results of the experiments show that the Deep Learning approach is more effective than classical approaches and generates better classification results.
Design and Implementation of Kiwifruit Seedling Tracing System Based on QR Code and Electronic Label

In the traditional cultivation of kiwifruit varieties, there are many varieties of kiwifruit with great differences. The defects of long breeding cycle and large investment of excellent kiwifruit make the cost of cultivating excellent kiwifruit extremely large. The cultivation process includes branch, root and skin grafting, cutting, Twig Cutting, hardwood cutting and many other links, which have extremely strict requirements on time. Manual record is not only inefficient, time-consuming and laborious, but also unable to achieve accurate breeding. By using the combination of two-dimensional code and electronic tag, kiwifruit cultivation is automated and information zed. In the process of mass recording and identification, electronic tag is used to record information, and RFID reader is used to write and read the mass information, so as to realize the input and tracing of seedling information; In the decentralized and small-scale information query links without RFID writing and reading conditions, two-dimensional code is used for reading. Therefore, it is of great significance to combine the electronic label and QR code to realize the automatic tracing of Kiwifruit in the whole process, which can greatly improve the efficiency of kiwifruit breeding management.
FPGA Implementation of Random Feature Mapping in ELM Algorithm for Binary Classification

Extreme learning machine (ELM) is a single layer feedforward neural network algorithm used for classification problems due to its accuracy and speed. It provides a robust learning algorithm, free of local minima, suitable for high-speed computation along with fast learning speed. In this paper, ELM algorithm implementation on hardware and software is discussed. A low-cost hardware implementation of 16-bit H-matrix generation on FPGA is discussed in the paper. Hardware implementation is carried out on Nexys-4 board using MATLAB and hardware description language (HDL). Generation of H-matrix is carried out using two activation functions, piecewise log-sigmoid and piecewise tan-sigmoid. This paper aims at optimizing the hardware implementation of ELM algorithm by minimizing the utilized resources of the FPGA. Finally, the ELM algorithm accuracy and hardware utilization for both activation functions are compared.
Machine Learning Techniques for Homomorphically Encrypted Data

The application of cloud-oriented services has increased from personal use such as mailing systems to the medical field. Typically, a cloud service provider offers encryption services of sensitive data. However, decrypting this data in the cloud for data analysis using machine learning can lead to privacy concerns. To this end, learning over encrypted data without decrypting it is the need of the future. In this article, we present a holistic review and summarize the literature on homomorphic encryption, related issues, applications in machine learning over encrypted data, and potential future research directions.
Plenary: From Traditional to Active Mathematics Courses: breaking the paradigm in Guatemala

This plenary is a discussion about the efforts, made by Universidad Galileo, to deal with the lack of solid high school education in mathematics of the first-year engineering students. Such efforts range from innovative supplementary courses to the design and implementation of active learning methodologies in the first mathematics courses. To assess its effectiveness, both academic and motivational variables were measured. The obtained results lead us to design different methodologies for part-time and full-time students, work to reduce anxiety levels that students present before the tests, and reinforce the link between mathematics and engineering minimizing the time invested in lectures, making the students the protagonists of their learning.
The effect of reading comprehension questions on linguistic predictors of readability

Readability formulae have been used to predict text difficulty, but their construction has been challenged. Previous readability formula designers typically adopted correct responses to reading comprehension questions (RCQs) as dependent variables, but they did not assess the effect of linguistic predictors concerning RCQs in the formula. Thus, this study self-designed a Chinese reading comprehension test, sampled 347 Chinese students in grades 7 and 8 as test-takers, and constructed readability formulae of whole-passage and answer-section models. Correlation and regression analysis showed that the effect of RCQs on the linguistic predictors of readability biased the typical whole-passage model when the model was used to predict text difficulty. We suggest researchers construct updated readability formulae by analysing the effect of RCQs on linguistic predictors of answer-section text to accommodate the present standardised reading tests. We also call for the construction of a readability formula for non-test library reading materials.
A Secure, Energy-Efficient and Distributed manageable model for a Smart Home

Continuous service provision, minimization of energy consumption and improvement of the provided security are the three fields that are tormenting sensors in a smart home. In the current work we propose and evaluate a model that continue to provide its services in a smart home environment even in the case of a fault. Our proposed solution achieves high energy efficiency and guarantees minimum security specifications that describe a reliable smart home model by utilizing blockchain.
Threat Detection with Facial Expression and Suspicious Weapon

Recently criminal activities such as robbery with the threat of life using weapons have increased exponentially. In the past, CCTV cameras were used for providing proof of criminal activities. But due to the technological advancements in areas like deep learning, image processing, etc., various ways are coming into the picture to prevent those criminal activities. Weapon detection was the first criteria introduced to define an activity as “suspicious”, but it had many drawbacks, such as dummy weapons were being classified as a threat and also the system failed where there was frequent use of weapons. As the phrase “Suspicious Activity” is a relative entity, human being can identify a friendly environment, where people are carrying weapons but the motto is not harmful. On the other hand, existing technology lacks this context. Here, we have tried to align the existing system of weapon detection and facial expression with this context. This enhances the ability of the system to take decisions as if it is thinking as a human brain. Our CNN model inspired by the VGGNet family named as suspExpCNN model optioned accuracy 66% was yielded for facial expression detection and Faster RCNN and SSD models yielded for weapon detection with accuracy 82.48% and 82.84%, respectively. The work further combined suspExpCNN model with weapon detection model and generated alert if an angry, scared or sad expression is detected.
Learning-based Blind Detection of Interference Parameters for NAICS System

In this paper, a supervised-learning based blind detection of interference parameters for network-assisted interference cancellation and suppression (NAICS) system is proposed. In order to enable joint detection or interference cancellation at a user equipment in NAICS system, we consider detection of the interference parameters including traffic-to-pilot power ratio (TPR), rank indicator (RI), and precoding matrix indicator (PMI). We divide overall process for detection of these interference parameters into two steps, and propose supervised learning based neural network architecture for detection of corresponding parameters in each steps. Link-level simulation results are provided to validate detection performance of the proposed neural network architecture and performance of NAICS system which uses the proposed learning based detection method with respect to block error rate (BLER).
Towards Designing an Effective Complexity Indicator for Audio Steganography

In the field of steganography, to effectively hide the secret message, it is of great importance to determine which part of the steganographic cover is suitable for embedding. Currently, most of the existing works focus on the image cover, while few works touch the audio cover case. In this work, we attempt to characterize the complexity of audio for selecting the steganographic cover. Specifically, the original cover is first convoluted with a specially designed adaptive convolution kernel. Based on the residual between the original and the convoluted audio, we derive a quantity for measuring the complexity of each frame for a given audio clip. Experimental results verify the usability of the proposed complexity indicator, suggesting high-complexity audio cover is favorable for data embedding. It is also found that the proposed complexity indicator could further boost the steganographic performance of the state-of the-art audio steganography methods. The source code is publicly available at https://github.com/capzxy/audio-complexity.
Dynamic Multi-level Attention Models for Dialogue Response Generation

One of the key challenges for creating a successful chat bot is to find an effective way to learn from human-human conversation data. Recently, a few neural network based dialog models, including the RNN language model (RNNLM) and the hierarchical recurrent encoder-decoder (HRED) model have shown promising results on dialog response generation. However, there is a critical challenge that the responses generated by these models incline to chit-chat style instead of being informative. In this paper, we empirically investigate this problem and also propose multilevel attention models to extend HRED with a hope that the attention mechanism can capture more informative content. The experiment studies on two multi-turn dialogue Datasets have shown the model’s potential.
TPCx-BB (Big Bench) in a Single-Node Environment

Big data tends to concentrate on the data volume and variety which requires large cluster capabilities to process diverse and heterogeneous data. Currently, NoSQL/Hadoop-based cluster frameworks are known to excel at handling this form of data by scaling across nodes and distributed query processing. But for certain data sizes, relational databases can also support these workloads. In this paper, we support this claim over a popular relational database engine, Microsoft* SQL Server* 2019 (pre-release candidate) using a big data benchmark, BigBench. Our work in this paper is the industry first case study that runs BigBench on a single node environment powered by Intel® XeonTM processor 8164 product family and enterprise-class Intel® SSDs. We make the following two contributions: (1) present response times of all 30 BigBench queries when run sequentially to showcase the advanced analytics and machine learning capabilities integrated within SQL Server 2019, and (2) present results from data scalability experiments over two scale factors (1 TB, 3 TB) to understand the impact of increase in data size on query runtimes. We further characterize a subset of queries to understand their resource consumption requirements (CPU/IO/memory) on a single node system. We will conclude by correlating our initial engineering study to similar research studies on cluster-based configurations providing a further hint to the potential of relational databases to run reasonably scaled big-data workloads.
Efficient Learning of Model Weights via Changing Features During Training

In this paper, we propose a machine learning model, which dynamically changes the features during training. Our main motivation is to update the model in a small content during the training process with replacing less descriptive features to new ones from a large pool. The main benefit is coming from the fact that opposite to the common practice we do not start training a new model from the scratch, but can keep the already learned weights. This procedure allows the scan of a large feature pool which together with keeping the complexity of the model leads to an increase of the model accuracy within the same training time. The efficiency of our approach is demonstrated in several classic machine learning scenarios including linear regression and neural network-based training. As a specific analysis towards signal processing, we have successfully tested our approach on the database MNIST for digit classification considering single pixel and pixel-pairs intensities as possible features.
Modular transfer learning with transition mismatch compensation for excessive disturbance rejection

Underwater robots in shallow waters usually suffer from strong wave forces, which may frequently exceed robot’s control constraints. Learning-based controllers are suitable for disturbance rejection control, but the excessive disturbances heavily affect the state transition in Markov Decision Process (MDP) or Partially Observable Markov Decision Process (POMDP). This issue is amplified by training-test model mismatch. In this paper, we propose a transfer reinforcement learning algorithm using Transition Mismatch Compensation (TMC), that learns an additional compensatory policy through minimizing mismatch of transitions predicted by the two dynamics models of the source and target tasks. A modular network of learning policies is applied, composed of a Generalized Control Policy (GCP) and an Online Disturbance Identification Model (ODI). GCP is first trained over a wide array of disturbance waveforms. ODI then learns to use past states and actions of the system to predict the disturbance waveforms which are provided as input to GCP (along with the system state). We demonstrated on a pose regulation task in simulation that TMC is able to successfully reject the disturbances and stabilize the robot under an empirical model of the robot system, meanwhile improve sample efficiency.
CNN based HDR/WCG Characteristic Detection for Ultra-High Definition Video

Featuring high bandwidth and low latency, the fifth-generation (5G) network can handle Ultra-High Definition (UHD) video services including 4K and 8K with High Dynamic Range (HDR) and Wide Color Gamut (WCG). In order to ensure high quality for consumers, the parameters of UHD video contents such as resolution, bit depth, frame rate, color gamut and etc. are usually signaled during the whole workflow to keep quality consistency and interoperability. When the signaling is unavailable or unreliable, the end users are likely to obtain unpleasant quality. In this paper, we propose a novel framework for CNN based luminance and color characteristic detection to detect the actual UHD video parameters directly from pixels rather than signaled parameters, which consists of two image classification networks to capture high-level video feature representation. By utilizing Multi-Class Image Reference (MCIR) and Local Feature Extraction and Fusing (LFEF) sub-networks, our scheme performs well on color gamut and dynamic range detection. Experiments on practical video contents show that our system can achieve accuracy of 99.4% and 94.9% on color gamut and dynamic range classification separately.
Efficient Logistic Regression with L2 Regularization using ADMM on Spark

Linear classification has demonstrated success in many areas of applications. Modern algorithms for linear classification can train reasonably good models while going through the data in only tens of rounds. However, large data often does not fit in the memory of a single machine, which makes the bottleneck in large-scale learning the disk I/O, not the CPU. In this paper, we describe a specific implementation of the Alternating Direction Method of Multipliers (ADMM) algorithm for distributed optimization. This implementation runs logistic regression with L2 regularization over large datasets and does not require a user-tuned learning rate meta-parameter or any tools beyond Spark. We implement this framework in Apache Spark and compare it with the widely used Machine Learning LIBrary (MLLIB) in Apache Spark 2.4
Accountability in AI as Global Issue

Artificial Intelligence are a global phenomenon increasingly crucial in economic development and leadership in this field. At first to make possible effective communication on this argument definition are needed as a first step of knowledge in order of regulate this and safeguard citizensâ rights. In 8 April 2019 High Level Expert Group on Artificial Intelligence set up by EC published this text. âArtificial Intelligence (AI) refers to systems that display intelligent behaviour by analysing their environment and taking actions with some degree of autonomy to achieve specific goalsâ.AI base system can be purely software- based acting in Virtual world (e.g. voice assistants, image analysis, software, search engines, speech and face recognition systems) or I can be embedded in hardware devices (e.g. advanced robots, autonomous cars, drones or internet of things applications)â.
A Successive Framework: Enabling Accurate Identification and Secure Storage for Data in Smart Grid

Due to malicious eavesdropping, forgery as well as other risks, it is challenging to dispose and store collected power data from smart grid in secure manners. Blockchain technology has become a novel method to solve the above problems because of its de-centralization and tamper-proof characteristics. It is especially well known that data stored in blockchain cannot be changed, so it is vital to seek out perfect mechanisms to ensure that data are compliant with high quality (namely, accuracy of the power data) before being stored in blockchain. This will help avoid losses due to low-quality data modification or deletion as needed in smart grid. Thus, we apply the parallel vision theory on the identification of meter readings to realize accurate power data. A cloud-blockchain fusion model (CBFM) is proposed for the storage of accurate power data, allowing for secure conducting of flexible transactions. Only power data calculated by parallel visual system instead of image data collected originally via robot would be stored in blockchain. Hence, we define the quality assurance before data uploaded to blockchain and security guarantee after data stored in blockchain as a successive framework, which is a brand new solution to manage efficiency and security as a whole for power data and data alike in other scenes. Security analysis and performance evaluations are performed, which prove that CBFM is highly secure and efficient impressively.