Reviewerly/exHarmony huggingface dataset:
Algorithm for extracting textural properties of specified image classes

Texture analysis plays an important role in the field of computer vision. For example, when classifying or segmenting images. To successfully complete the analysis task, it is important to use informative textural features. In this article, the authors assume that informative features correspond to the textural properties of images and propose an algorithm for extracting textural properties as informative textural features. The proposed algorithm is tested on a dataset consisting of 28 texture classes. The experimental results show that the use of the algorithm allows us to determine the features corresponding to the textural properties of the images under consideration. Classification of images by features corresponding to textural properties allows achieving a precision value of 77 % using 23 features for 23 classes.
Optimizations in computing the algebraic normal form transform of Boolean functions

The Reed-Muller transform is widely used in discrete mathematics and cryptography, in particular for computing the algebraic normal form of Boolean functions. This is a good reason to look for ways to optimize the implementation of the algorithm. Here we present different ways for optimization based on the bitwise representation of the true table vector of a Boolean function. We compare the implementation of the standard algorithm with the implementation using AVX2 and AVX512 instruction sets and the corresponding extended registers and parallel implementation using GPUs with CUDA and OpenMP. The experimental results show that various degree of speedup can be achieved depending on the used platforms and approaches.
Local Tetra Pattern and Its Benefits to Improve the Performance of Car and Pedestrian Detection Under Hostile Conditions

Traffic detection is a topic of great interest in recent years due to a high demand for better traffic detection systems. Existing traffic detection algorithms work well under ideal driving conditions, however their performance decreases under difficult conditions such as insufficient lighting and illumination. Recently, local patterns have been successfully applied in order to handle complex texture conditions, such as stereo matching, and texture classification. We propose a method that applies Local Tetra Pattern for data preprocessing, so as to improve the performance of deep learning models under said conditions. Our approach achieved better performance than the original raw-models while the changes in inference time are maintained within a negligible interval. By fusing local patterns and raw images, the model gains an acquisition of discriminative information in regions that are highly similar. In challenging conditions, these kinds of information are essential for the model to recover its consciousness of concerned objects which cause many re-cognitional obstructions. Experimental results show a percentage as high as 35.847%, an increase of 12.575% in comparison with the original result on the SKKU data set.
Review on the Application of Blockchain Technology to Compact COVID-19 Pandemic

The sudden explosion of the COVID-19 pandemic shows the limitations of the existing healthcare system in handling health emergencies. Innovative technologies like blockchain can play an important role in effective planning operations for fighting coronavirus pandemic. Blockchain can provide solutions that ensure safe and reliable medical supply chain with a secure data management health system. In this paper, we present an extensive review on the use of blockchain technology to mitigate COVID-19 pandemic. The research papers are classified into two groups: proposer work with implementations and surveyed papers. Both are analyzed and summarized. Then, we discussed the importance of applying blockchain technology for the COVID-19 pandemic. Finally, the challenging and future research directions are identified to motivate researchers for more efforts in this domain.
Random Error Reduction in Phase Shift Interferometry

The object of research in this article is a new approach to reducing the error of phase demodulation in phase-shifting interferometry. It is based on averaging a combination of functionally related sequences of interference patterns intensities, with the help of which the phase is demodulated. It is possible to average both individual phase values at separate points of the interference pattern and their differences between different points. The method is based on finding the phase difference at three arbitrary points of the interference pattern. Fixing one or two points of the interference pattern with other points, scanning is carried out over its entire surface. Thus, the authors obtain a set of phase differences between different points. Averaging them allows to reduce the influence of noise on the measurement result. Thus, for each point of the interference pattern, a set of phase values or phase differences is obtained. Using the found phase differences, it is also possible to determine the phase directly at each point of the interference pattern. In contrast to the known methods, the proposed approach does not require registration of additional interference patterns. This allows to shorten the measurement time without losing their accuracy.
Nurturing the Rudiments and Use Cases of Ongoing Natural Language Generation for a Future Profitable Business More Profitable

Decoding the world of artificial intelligence and its usage in the current intelligence landscape enhance bottom-up growth in building resilient global business. The areas of artificial intelligence (AI) concerned with human-to-machine and machine-to-human interaction. The Next Wave in AI-driven speech is Natural Language Generation (NLG). Natural Language Generation (NLG) is the use of artificial intelligence (AI) programming to produce written or spoken narrative from a dataset. NLG is related to computational linguistics, natural language processing (NLP) and natural language understanding (NLU). NLG research often focuses on building computer programs that provide data points with context. Sophisticated NLG software has the ability to mine large quantities of numerical data, identify patterns and share that information in a way that is easy for humans to understand. The speed of NLG software is especially useful for producing news and other time-sensitive stories on the internet. At its best, NLG output can be published verbatim as web content. The goal of Natural language generation (NLG) is to use AI to produce written or spoken narrative from a dataset. Therefore, this study aims to study how NLG enables machines and humans to communicate seamlessly, simulating human to human conversations and using NLG how organizations are building new customer experiences, monetizing information assets, introducing new offerings and streamlining operational costs. Therefore, the coverage of this chapter will answer to the industrialists and new start-ups. What can NLG do for business? and what are the future applications of NLG?
TATA: Throughput-Aware TAsk Placement in Heterogeneous Stream Processing with Deep Reinforcement Learning

Data Stream Processing (DSP) applications, which generate real-time analytics on continuous data flows, have become prevalent recently. For the deployment of DSP applications, task placement is an important and essential part. As determining the optimal task placement is an NP-hard problem, several efficient heuristics have been designed and Deep Reinforcement Learning (DRL) was used to train the scheduling agent. Current DRL-based approach assumes all resources including CPU, memory and networking are homogeneous. However, the available computation and network resources are heterogeneous in many scenarios. To deal with it, we devise a general DRL-based resource-aware framework, which models resources using graph embedding and attention mechanism to predict the placement. Furthermore, in order to accelerate the training process and improve the throughput, we propose an efficient throughput estimation tool, which can estimate the throughput with high accuracy. We integrated our scheduling heuristic framework into Apache Flink and conducted comprehensive testings using multiple synthetic and real DSP applications. The experimental results show that our framework increases the throughput by 64%, 42%, 29% on average respectively compared with three state-of-the-art strategies.
Geospatial Intelligence Analysis to Support National Defense Interests

Threats to national defense can occur in the form of military and non-military threats. The biggest threat and challenge for the Indonesian state, which has not yet been resolved, is terrorism. The Indonesian government has carried out the handling of terrorism, but it is still difficult to catch the perpetrators of terrorism. This research aims to provide an alternative to find out the hiding places of terrorists using the Geospatial Intelligence (GEOINT) approach. The limitation of this research is the mountainous area in the province of Central Sulawesi. The method used in this study is to use a GEOINT approach, which is a combination of remote sensing, geographic information systems (GIS), and cartography to obtain data and analyze results. The analysis was carried out using a weighted linear combination method. The quantification process was carried out on all spatial data used for each parameter related to the presence of terrorists in the mountains. Quantification is done by changing each subparameter class to a value in the range 1–5. Then each value will be weighted as a multiplier factor to get the final value. From the analysis and discussion results, it can be concluded that GEOINT analysis can be used as an initial study to find out the hiding places of terrorists.
Solar Tracker and Wind Guide Designs to Improve the Energy Conversion in a Hybrid Energy Harvester System

Abstract This study focused on improving the energy conversion of hybrid solar panels and a wind turbine system. The improvement was produced by enhancing the system with a solar tracker and a wind guide. A microcontroller operated the single-axis solar tracker based on a solar elevation database at -6.91N, 107.61E. The wind guide was an Omni Directional Guide Vane (ODGV), designed to support the drag-type Savonius turbine. The ODGV’s inside and outside diameters were 540 mm and 1000 mm, respectively. The wind guide had a 30° polar angle between fins and produced a torque of 0.128 Nm at a wind speed of 4 m/s. The solar tracker increased the system performance by 47% and the wind guide by 166%.
Making Sense of Cybersecurity in Emerging Technology Areas

Abstract Shortening technology development cycles in information and communication technology (ICT) make it imperative to anticipate the emergence and evolution of new computing technologies and ecosystems. A wide range of questions must be answered to ensure that new technology environments are viable, including the examination of usability, efficiency, usage models, security, and privacy. These contextual aspects of new technologies are essential for their adoption. They are also important to understanding the potential of new types of cybercrime and requirements for the development of mitigation techniques. However, we lack methodologies to model and predict the features of the evolving ICT ecosystems, and the requirements their evolution places on legal systems and regulatory frameworks. The absence of such models is a significant obstacle to creating consistent approaches necessary to forecast both the technology development and the trends in cybercrime. The chapter discusses which potential methodologies could be used for forecasting cybersecurity concerns in disruptive technology areas and trends in cybercrime in complex environments. It proposes that a unified approach should be developed for predicting cybersecurity effects of innovative technologies and trends in cybercrime. It first examines concepts associated with emerging technologies and their impact on cybersecurity. It then looks at approaches to modelling and analysis already developed in adjacent spaces, with a focus on knowledge representation and risk engineering, and analyses representative examples to illustrate the benefits these approaches can bring.
SocialSift: Target Query Discovery on Online Social Media With Deep Reinforcement Learning

Among the prohibitively large volume of posts (e.g., tweets in Twitter) on online social networks (OSNs), how to design effective queries to explore the ones of interest is a pressing problem. There are two main challenges to address the problem. First, given public application programming interfaces (APIs) for querying posts related to keywords from an extremely large vocabulary, how to infer the keywords relevant to our target interest using as few queries as possible? Second, how to deal with the agnostics of OSN's API? i.e., as different social networks typically have different running mechanisms, even with some randomness in returning results, how to build the knowledge of the API returns w.r.t. target interests from scratches? To address the above two challenges, we propose a target query discovery framework based on a deep reinforcement learning approach, named SocialSift. SocialSift intelligently interacts with OSNs' keyword-based API and develops its own knowledge in searching the optimal queries w.r.t. the target interests as well as OSN APIs. Specifically, to address the first challenge, we are inspired by the human searching experience, and recognize learning to query with context awareness to reduce the searching space, by qualifying keywords from returned results and keeping the tracks of the query trial history, or say contexts. As for addressing the second challenge, we treat OSNs' APIs as black boxes and probabilistically quantify query-interest pairs guided by rewards, which is a well-curated indicator w.r.t. target interests. Empirical results on three popular OSNs: Twitter, Reddit, and Amazon demonstrate our SocialSift significantly outperforms the state-of-the-art baselines by 12% in retrieving target posts.
Blockchain-based Model for Gene Data Management using De-Identifying Scheme

Improvements in genome technology have been substantially increasing the available amount of gene data. As the gene data contain sensitive personal information, security schemes that can ensure confidentiality and safe sharing are necessary. In this paper, we propose a model for secure gene data management using blockchain and encryption. The proposed model employs a storage where the gene data are partially encrypted, and access control for the data is ensured by the blockchain so that the data can be shared safely. Furthermore, the gene owner can trace the usage and status of the data using a decentralized application.
Real Time Face Mask Detection Using MobileNetV2 and InceptionV3 Models

The widespread outbreak of COVID-19 has resulted in the need for efficient face mask detection. This study is focused on achieving real time face mask detection using methods which are not computationally expensive. A dataset which contains images captured in real time was selected for this application. The subjects in the dataset belonged to three different categories consisting of mask worn correctly, mask worn incorrectly and no mask. Two pretrained Convolutional Neural Network models, MobileNetV2 and InceptionV3 were fine-tuned using the transfer learning approach. This paper presents a simple approach in which the Region of Interest is extracted using a pretrained Multi-task Cascaded Convolutional Neural Network, whose output is fed into the classifier model. It was observed that the InceptionV3 model performs better when compared with MobileNetV2 model.
Analysis of different types of word representations and neural networks on sentiment classification tasks

This paper evaluates and compares the performance of sentiment analysis using traditional vector representations to the word-embedding approach, and shallow networks to recurrent and gated neural networks. In the traditional approach, we explore ways the data can be presented in discrete space and how they perform on sentiment-analysis tasks. We compare their performances with the word-embeddings approach on the same sentiment analysis tasks where the words are represented in continuous-space. We use shallow machine-learning models, such as naïve bayes, nearest neighbor, stochastic gradient descent, decision tree, logistic regression, etc. in the traditional approach. For the word-embeddings approach, we apply - RNNs, LSTMs, and GRUs to perform the analysis. RNNs were used to overcome N-gram fixed window size limitation, and GRU and LSTM were used to overcome RNN's vanishing and exploding gradient problem and to capture long distance relationships. It was found that recurrent network models and word embeddings overall do better than the shallow networks and traditional word representations.
Full Text Search Setup on a Website

ntroduction. When implementing the search for text fragments on the site, approaches are used that are different in complexity and performance. There is also a sequence of related tasks: choosing a text indexing option, sending a text for indexing, selecting texts for indexing specifically from the CMS database, choosing a search engine, and others. These approaches do not always provide satisfactory search results. Purpose. The purpose of the article is to the description of existing solutions for full-text search on a website, their advantages, and disadvantages. Development of a full-text search algorithm using the Elasticsearch system. Methods. Analysis of approaches to the implementation of full-text search on a website, varying in complexity and performance. Identification of flaws and vulnerabilities in more primitive approaches and the development of more advanced and complex algorithms that eliminate the identified deficiencies. Step-by-step implementation of full-text search using third-party systems. Results. A method for implementing full-text search using Elasticsearch is described. The advantage of the new approach is the asynchronous sending of the page content and its address to a specific service responsible for communication with Elasticsearch. This allows you not to block the normal work with the CMS and not depend on the availability of the indexing service. The approach described in the article is flexible and adaptable for various website architectures. Asynchronous processing of indexing requests ensures high query execution speed and system fault tolerance. Conclusions. The article discusses various approaches to implementing full-text search on a website, their advantages and disadvantages. Based on the analysis, a more flexible and universal approach to the implementation of a full-text search system has been developed. A solution is proposed with step-by-step implementation and setup of advanced full-text search using Elasticsearch.
Edge Extraction of Ancient Books Based on Freeman Chain Code

With the passage of time and improper preservation methods, many ancient books and texts have disappeared in our world. How to preserve ancient books which carry excellent traditional Chinese culture has also become a problem of people's concern. One of the most popular discussions and the most modern methods is to digitize ancient books. This paper proposes a digital processing method for ancient books based on Freeman chain code. The process of the method is, firstly, to gray the acquired image with the maximum grayscale method, then to perform median filtering and morphological denoising to get the clear image edge. Finally, the processed image is extracted with the Roberts operator, and then the continuity of the edge is strengthened through Freeman chain code to obtain a clear and continuous edge of ancient text. The experimental results show that the proposed method can extract the text edges quickly and accurately, providing an effective method for further research on the digital processing of ancient books.
Improved Fuzzy Q-Learning with Replay Memory

Artificial Intelligence (AI) is becoming more and more popular after 2000s. Many AI-related technologies are developed and applied to our daily life which brought us benefits, such as Reinforcement Learning (RL) (L. P. Kaelbling, M. L. Littman, A. W. Moore: “Reinforcement Learning: A Survey”, 1996, Journal of Artificial Intelligence Research, Vol 4, (1996), 237–285; 9605103.) which allows the first computer to defeat human champion in Game Go. However, such AI technology are heavily depended on the Neural Network (NN) which is too complicated for human to understand and interpret. On the other hand, Fuzzy Logic (FL) is an AI technology that is well known for its interpretation, however, with relatively poor performance compare to Neural Network related methods. This paper is introducing an example of replacing NN with FL for optimizing RL, so that we can gain better performance and easier tuning process, and also with interpretation on the results.
Modelling of Flexible Manipulator System via Ant Colony Optimization for Endpoint Acceleration

Abstract The application of flexible manipulators has increased in recent years especially in the fourth industrial revolution. It plays a significant role in a diverse range of fields, such as construction automation, environmental applications, space engineering and many more. Due to the lightweight, lower inertia and high flexibility of flexible manipulators, undesired vibration may occur and affect the precision of operation. Therefore, development of an accurate model of the flexible manipulator was presented prior to establishing active vibration control to suppress the vibration and increase efficiency of the system. In this study, flexible manipulator system was modelled using the input and output experimental data of the endpoint acceleration. The model was developed by utilizing intelligence algorithm via ant colony optimization (ACO), commonly known as a population-based trail-following behaviour of real ants based on autoregressive with exogenous (ARX) model structure. The performance of the algorithm was validated based on three robustness methods known as lowest mean square error (MSE), correlation test within 95% confidence level and pole zero stability. The simulation results indicated that ACO accomplished superior performance by achieving lowest MSE of 2.5171×10 −7 for endpoint acceleration. In addition, ACO portrayed correlation tests within 95% confidence level and great pole-zero stability.
An “embedded” access mode of copyright resources in knowledge aggregation service portal

Knowledge service is the key link to promote the upgrading of science and technology innovation, professional education and publishing industry. This paper mainly expounds the access mode of knowledge resources on knowledge aggregation and service platform. The classification and investigation of the massive knowledge resources are carried out. This paper proposes an “embedded” resource access mode of knowledge service platform, which solves the common problem of copyright authorization in the process of resource access for building knowledge aggregation and service platform, and provides a solution for copyright organizations to realize self-supporting without handing over copyright. Based on the “embedded” access mode, Wanfang Data has been implemented on its knowledge service platform, and has been launched the resource system aggregation service called “Station’s Application”.