Reviewerly/exHarmony huggingface dataset:
Reinforcement Learning in Latent Action Sequence Space

One problem in real-world applications of reinforcement learning is the high dimensionality of the action search spaces, which comes from the combination of actions over time. To reduce the dimensionality of action sequence search spaces, macro actions have been studied, which are sequences of primitive actions to solve tasks. However, previous studies relied on humans to define macro actions or assumed macro actions to be repetitions of the same primitive actions. We propose encoded action sequence reinforcement learning (EASRL), a reinforcement learning method that learns flexible sequences of actions in a latent space for a high-dimensional action sequence search space. With EASRL, encoder and decoder networks are trained with demonstration data by using variational autoencoders for mapping macro actions into the latent space. Then, we learn a policy network in the latent space, which is a distribution over encoded macro actions given a state. By learning in the latent space, we can reduce the dimensionality of the action sequence search space and handle various patterns of action sequences. We experimentally demonstrate that the proposed method outperforms other reinforcement learning methods on tasks that require an extensive amount of search.
A/B testing as an effective instrument for adaptation of the user interface at the iterative model of developing applications for mobile devices

Changing the user interface always entails risks of decreasing application ergonomics and user churn. The purpose of this work is to find methods for changing the user interface without the need to use and increase the number of active users. Using Alpha/Beta testing and analysis of application metrics, it is possible to include design in each iteration and remotely manage the application configuration, which improves the user interface of the application. The way to integrate the UI modification and adaptation phase into an iterative software development model is proposed in this article. The successful user interface improvement experiments that have improved application metrics are described. In this work, the problem was highlighted, lack of information and control over user purchases, which negatively affected the number of purchases. A hypothesis is put forward that the user must have an effective visualization and control tool in the application. As a result of the experiment, the hypothesis was confirmed by an increase in the number of purchases and user activity. There was also a data security issue in the application, which put users at risk of data loss. An experiment was conducted to change the default value of user settings, which led to an increase of the positive metrics for using the data reservation functionality. The problem of polling users is also considered, which is an important component in the process of improving the ergonomics of an application. The method of remote user polling was used, which allowed to receive quick feedback from users and quickly respond to requests from users. The result of the work is the confirmation of the hypothesis of changes in the user interface in the cycles of the iterative development model, the positive dynamics of application metrics, as well as the satisfaction of users with the changes.
Efficient Feature Extraction from Multispectral Images for Face Recognition Applications: A Deep Learning Approach

Abstract In recent years many face recognition algorithms were used for the identification and authentication of a person to a system. However, still, feature extraction from multispectral images was considered to be a challenging task. Feature extraction, including highlight location and portrayal, assumes a significant job in real-time security-based applications. In this paper, a novel Geometric Algebra-based Multivariate Regression Feature Extraction (GA-MVRFE) algorithm was proposed to extract features from a huge dataset stored in the cloud efficiently. This proposed algorithm works with the supreme expedient deep learning approach - Convolutional Neural Network (CNN) for image classification. CNN will automatically detect significant features from the multispectral images without any human intrusion from a huge database. Real-time images were captured with three different cameras and applied filters over the images and were created as a dataset. To show the competence of the proposed algorithm, an exclusively created dataset with a set of 14,400 image data was applied in the proposed and other existing algorithms, and their efficiency and robustness were noted. Providentially, GA-MVRFE produced better accuracy in ‘Face Recognition’ with a less time fraction compared with former algorithms. Obtained accuracy % for Geometric Algebra Oriented fast and Rotated Brief (GA-ORB), Geometric Algebra Fast Retina key-point Extraction Algorithm (GA-FREAK), Trilateral Smooth Filtering (TRSF), Cross Regression Multiple View Features extraction (CRMVF) and GA-MVRFE was 87.81, 83.23, 90.72, 91.67 and 97.57 respectively.
Corder

The intrinsic irregular data structure of graphs often causes poor cache utilization thus deteriorates the performance of graph analytics. Prior works have designed a variety of graph reordering methods to improve cache efficiency. However, little insight has been provided into the issue of workload imbalance for multicore systems. In this work, we identify that a major factor affecting the performance is the unevenly distributed computation load amongst cores. To cope with this problem, we propose cache-aware reordering (Corder), a lightweight reordering algorithm that facilitates workload balance as well as cache optimization. Comprehensive performance evaluation of Corder is conducted on various graph applications and datasets. We observe that Corder yields speedup of up to 2.59× (on average 1.47×) over original graphs.
Pandemic and Post-Pandemic Use of Immersive Learning Technology

Immersive learning technology has the potential to increase student engagement and learning. With the onset of the pandemic in March of 2020, the delivery of education changed, and the use of immersive learning technology was impacted. This chapter considers charges and impacts at the K-12, CTE, and post-secondary level—with on-site, remote, and hybrid learning models—during the COVID-19 pandemic. Anticipated trends in education post-pandemic include an increased need for personalized learning; continued growth in remote learning, virtual learning, and online content and resources and increased demand for career and technical education. Consideration is also given to the implications for immersive learning technology post-pandemic. Specific consideration is given throughout the chapter to the use of zSpace in the United States.
Human Activity Recognition by Deep Convolution Neural Networks and Principal Component Analysis

Human activity recognition (HAR) is a domain that has a great deal of interest in recent years. Because it’s used in various application domains like (critical situations for the elderly or Firefighter, detect about injured in different sported and other application in domain Medicinal, Military, and Technological). Moreover, HAR are categorized by video, images or sensors. The multi-sensors that stabilized on the human’s body provides raw data. The raw data consist of multi-channel in a pattern time series signals and activities that are predefined. There are so many types of sensors which are utilized in HAR such as accelerometers, gyroscopes, magnetometer or electrocardiogram. For automatic feature extraction from this data to the classification of the activities, we propose new design of convolutional neural networks (CNNs) and Principal Component Analysis (PCA). The proposed system is estimated using dataset five datasets; (Mhearth), (Sensors-Activity-Recognition-Dataset-SHOAIB), (REALDISP), (REALWORLD) and (Activity Recognition Dataset), where the accuracy equals 99.8, 99.44, 99.85, 96.90, 98.68%, Sequentially.
Privacy Protection Method of Electric Power Network Based on Blockchain

Abstract In order to ensure the integrity and confidentiality of power data in the era of big data, it is very important to protect the privacy of smart grid data information. Analyzing electricity big data can obtain electricity consumption habits of electricity users to guide the formulation of power supply plans. This experiment uses an asymmetric encryption algorithm. The encryption and decryption of the asymmetric encryption algorithm use two different keys to fully guarantee the security of the blockchain system. The experiment is based on the blockchain technology in photovoltaic microgrid electricity trading, which belongs to the current type of trading, which is divided into five stages. The service provider is a node of the blockchain system. All smart contracts are encrypted by the private key of both parties in the transaction and the public key of the service provider, which fully guarantees the privacy of users. The experimental data shows that the smart contract is decrypted by the private key and the public key of both parties, so as to obtain the relevant information of the transaction and make the corresponding power dispatch work during the transaction period.The blockchain system solves the security and privacy issues of both parties in the absence of a third-party intermediary. Therefore, the use of blockchain technology can achieve a higher degree of automation for point-to-point power transactions.
The Influence of Gender on Performance in Gaming Environment with Different Typographic Factors

The demographic factors like age and gender are the significant attributes to be considered in usability. The different gender and age will have different perceptions about the usability of any user interface. The primary objective of this study is to find out the influence of gender in the gaming environment with different typographic factors. To investigate the influence of gender, a gaming prototype is developed with different typographic factors i.e Font, Font-size, and Font-Case followed by a questionnaire on Likert-Scale. The participants were given two basic tasks, i.e. Clicking and Text Selection with different typographic factors for experiments. 84 participants from different academic venues participated in experiments. The collected data is analyzed through different statistical analysis methods. It is observed that significant differences are found between males and females. This study may pave new research directions to explore the impact of these factors in multiple different contexts.
A Study of Internet Map Building Recognition Based on AI Technology

A solution for building objects recognition from Internet map is proposed. Map pages are separately screenshot with customized CSS style and spliced together. Building contours are detected with Suzuki's contour tracing algorithm. Annotation points of text labels are detected with Hough Transform. Text labels in the image are first detected with AdvancedEAST algorithm, and then recognized as characters with CRNN algorithm, which is fine-tuned with specific data set in our works. A fast processing architecture design based on distributed parallel computing is proposed to efficiently accomplish the whole recognition process.
Reminiscences of a Classical Model Expert Elicitation Facilitator

In this chapter, I trace my introduction to the Classical Model and to the thoughts and philosophy of Roger Cooke, and then go on to recount some experiences of acting as a facilitator in many real world and sometimes crucial expert elicitations. The essence of my own history is that it took me a very long time to start to understand, and appreciate, the elegance of Roger's Classical Model (Cooke ), its mathematical probity and how it is best deployed in application. I am sure I still haven't fully mastered the probability calculus entirely, and don't doubt others might quarrel with my preferred way of conducting elicitations that rely on the Classical Model, using a plenary workshop approach. This said, I can't find, devise or even imagine, a better alternative to the Classical Model. And, on top of this extraordinary intellectual achievement, Roger Cooke has been a beneficent collaborator nonpareil, always willing to help me, and anyone else, avoid self-inflicted elicitation and probabilistic infelicities.
The Influence of Biodegradable Polymer Materials on Heavy Metal Activity in Soil Based on Analysis of Big Data

Abstract Biodegradable polymer material is a new type of slow-release organic fertilizer. It contains many nutrients, and has a long degradation cycle. at the same time, It can provide nutrients continuously, and the degradation products are pollution-free, which can significantly reduce the negative impact on the environmental system. According to big data analysis, biodegradable polymer materials can not only provide nutrients during the degradation process, but also solidify heavy metal ions and reduce their activity by increasing soil pH and increasing soil available phosphorus and organic matter content. This article takes biodegradable polymer materials rich in various nutrients as the research object. Through leaching experiments, the environmental impact of biodegradable polymer materials on soil was studied, which provided a theoretical basis for the development of economical and practical chemical remediation techniques for heavy metal contaminated soil [1-2] .
Wireless Scoreboard Technology Architecture for Athlete Performance Data Warehouse at Multiple Table Sports Games

Abstract The presence of technology brings many new changes that facilitate human life. In the field of information for sports competition results in the form of a scoreboard. Scoreboard is used in sports tournaments as a display of the results of a tournament. So everyone who is in the arena can find out the results of the tournament score. Scoreboard generally displays the value of the tournament, displays the time of the tournament and the number of rounds during the tournament. The majority of scoreboard shows only one result from one tournament, while there are tournaments where several teams compete simultaneously in one room, so the audience has difficulty knowing the score of other tournaments even though they are in one room. For example, in a pool or table tennis tournament, in one arena there are more than one team competing. At that time, usually the scoreboard is only on each table. That causes a limited number of scoreboard that can be seen by the audience, not all tournaments. Meanwhile, the scoreboard used in Indonesia also still uses the remote and cable as the transmission media when inputting scores. The remote is held to a distance according to the length of the cable which is very impractical. The space for the remote holder is also not flexible because the remote is connected by a cable. Therefore, in inputting the score there is no need to use cable anymore, because this application is web-based and connected via a wireless network. The purpose of this research is: to make it easier to record scores of tournaments with multiple tables, and to record performance statistics from athletes. The benefits of this research are: to make it easier to record values because they are connected to a wireless network and display them in real time, and get athlete performance statistics during the competition.
A Deep Learning Approach for Detecting and Classifying Cancer Types

Cancer is one of the deadly diseases worldwide. It takes a lot of time and effort to detect it at the early stages. Various pathological and imaging techniques are widely used by doctors/physicians to ensure the presence of cancerous cells in the body. The techniques currently being used are highly time-consuming and lack accuracy. This paper deals with applying deep learning algorithm to detect the presence of different types of cancer. Brain, lung, and skin cancer are determined using Convolution Neural Networks (CNN). By utilizing pre-trained weights of VGG16, the model is trained and fine-tuned with cancerous and normal CT scan images. Experimental evidence shows that the adopted transfer learning (such as using pre-trained weights of VGG16) method provides better accuracy than traditional methods. The accuracy obtained is greater than 95%.
Integrating GPU Support for FreeSurfer with OpenACC

FreeSurfer is among the most widely used suites of software for the study of cortical and subcortical brain anatomy. However, analysis using FreeSurfer can be time-consuming and it lacks support for the graphics processing units (GPUs) after the core development team stopped maintaining GPU-accelerated versions due to significant programming cost. As FreeSurfer is a large project with millions of source lines, in this work, we introduce and examine the use of a directive-based framework, OpenACC, in GPU acceleration of FreeSurfer, and we found the OpenACC-based approach significantly reduces programming costs. Moreover, because the overhead incurred by CPU-to-GPU data transfer is the major challenge in delivering GPU-based codes of high performance, we compare two schemes, copy-and-transfer and overlapped-full-transfer, to reduce such data transfer overhead. Experimental results show that the target function we accelerated with overlapped-full-transfer scheme ran 2.3× as fast as the original CPU-based function, and the GPU-accelerated program achieved an average speedup of 1.2× compared to the original CPU-based program. These results demonstrate the usefulness and potential of utilizing the proposed OpenACC-based approach to integrate GPU support for FreeSurfer which can be easily extended to other computationally expensive functions and modules of FreeSurfer to achieve further speedup.
Research on Environmental Target Image Recognition Method of Coal Mine Rescue Autonomous Robot

Aiming at the problems of redundant computation and poor real-time performance in searching key points of visual image in standard SIFT algorithm, an Improved SIFT algorithm is proposed and applied to coal mine rescue autonomous robot to realize environment information perception, target recognition and matching in coal mine underground environment. In this method, Mahalanobis distance is used to replace the Euclidean distance and simplify the feature points in the traditional SIFT algorithm, so as to enhance the real-time performance of SIFT algorithm in recognizing coal mine environment targets and matching. The experimental results demonstrate that the improved algorithm greatly improves the real-time performance of visual recognition and the accuracy of target matching.
DistStream: An Order-Aware Distributed Framework for Online-Offline Stream Clustering Algorithms

Stream clustering is an important data mining technique to capture the evolving patterns in real-time data streams. Today’s data streams, e.g., IoT events and Web clicks, are usually high-speed and contain dynamically-changing patterns. Existing stream clustering algorithms usually follow an online-offline paradigm with a one-record-at-a-time update model, which was designed for running in a single machine. These stream clustering algorithms, with this sequential update model, cannot be efficiently parallelized and fail to deliver the required high throughput for stream clustering.In this paper, we present DistStream, a distributed framework that can effectively scale out online-offline stream clustering algorithms. To parallelize these algorithms for high throughput, we develop a mini-batch update model with efficient parallelization approaches. To maintain high clustering quality, DistStream’s mini-batch update model preserves the update order in all the computation steps during parallel execution, which can reflect the recent changes for dynamically-changing streaming data. We implement DistStream atop Spark Streaming, as well as four representative stream clustering algorithms based on DistStream. Our evaluation on three real-world datasets shows that DistStream-based stream clustering algorithms can achieve sublinear throughput gain and comparable (99%) clustering quality with their single-machine counterparts.
Application of Computational Complexity Theory to Pornographic Detection in Live Based on Frame Sampling

In order to prevent people from troubling by the sensitive information in the Internet, it is urgently compulsory to fortify the supervision upon Internet culture. Our works mainly focus on the online-live and internet short videos realtime invigilation. In this paper we leverage a novel deep learning (DL) model which is constructed by using Late Fusion method, also combining frame sampling to detect the sensitive information in stream mode. Plus, we use computational complexity theory to optimize the classification accuracy of specific samples. Our experiment shows that it is feasible to apply the theory into the DL model. After the optimization, the result has a certain improvement in classification accuracy compared with the naïve form.
Kannada word sense disambiguation using semantic relations

Abstract In this paper, a method is proposed to use the semantic relations of words to solve Word Sense Disambiguation (WSD) for the Kannada language. Semantic relationships like hypernymy and hyponymy are used in finding the overlaps between two concepts. Integrating the idea proposed in this paper with original Lesk’s technique improves the accuracy of the Lesk’s algorithm. Indo-WordNet is used as the lexical resource to gain the knowledge about the semantic relations of words and this knowledge is exploited to disambiguate the target word of the Kannada language. Since the approach used in this paper is unsupervised, so it can be scalable to other languages also.
Image-Based Wind Power Curve Data Cleaning Algorithm via the Matching of Deformation Template

The Supervisory Control And Data Acquisition (SCADA) system can collect the wind turbine data, which plays an important role in wind farm maintenances and operations. Due to some unforeseen circumstances, the wind power curve (WPC) contains many abnormal data which should be cleaned. This paper proposed an image-based WPC data cleaning algorithm via the matching of deformation template (MDT). The proposed method can be summarized into the following steps: Initially, the unprocessed data are cleaned by the Local Outlier Factor (LOF) algorithm in three-dimension space (wind speed, rotor speed, wind power). Then, the pre-processed WPC data is transformed into a WPC image with a mapping between the data and the pixels. The reference WPC is aligned to the WPC image via affine transformation based on the bounding box of the contour. In the third step, a feature image representing the confidence of being normal is built based on the distances in eight directions. In this way, the data cleaning task is transformed into an image segmentation problem. The image thresholding method is employed and the optimal threshold t <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> is determined by the dissimilarity of Hu moment with the deformed reference WPC. Comparative experiments were operated with some typical data-based and image-based algorithms on the datasets from 17 wind turbines to verify the effectiveness and versatility of the proposed method.