Reviewerly/exHarmony huggingface dataset:
Clustering Reduction Method Analysis of Rough Set and Decision Tree based on Weight Matrix Analysis

Abstract Decision table is a kind of special and important knowledge representation system. In this paper, rough set theory is used to discretize data and reduce data and rules. From the definition of resolution matrix, it can be seen that the shorter the length of an item in the resolution matrix, the greater the effect of this item on classification. And the more frequently the item appears, the more important it is decision tree. Construction of decision tree is a cyclic recurrence process. The paper presents clustering reduction method analysis of rough set and decision tree based on weight matrix analysis.
The Application of Artificial Intelligence in the Evaluation of Linear Thinking Mode of Chinese Piano Music

Aiming at the problem of poor reliability of traditional thinking mode evaluation methods, the application of artificial intelligence in linear thinking mode evaluation of Chinese piano music was studied. Using artificial intelligence technology to extract the characteristics of Chinese piano music, according to the extracted characteristics to establish the evaluation index system of Chinese piano music linear thinking mode. The analytic hierarchy process (AHP) was used to determine the weight of each evaluation index, and the linear thinking mode was evaluated according to the principle of fuzzy mathematics.
Academic writing as an academic discipline: problematic issues

The paper studies peculiarities of introducing the course of “Fundamentals of Academic Writing,” into the Ukrainian education and scientific process. The integration of the Ukrainian science into the European space caused the necessity of including “Academic Writing” into the Ukrainian curricula. Fundamentals of a new academic discipline are represented through the prism of world and national achievements, which determine such concepts as “academic communication”, “academic etiquette”, “academic integrity”, “the quality of academic text”. This research focuses on the following major issues: What is the essence of “Academic Writing”? Is it a brand new course? How does it affect the ranking of higher education institutions? How should intertextual tools be used to keep the academic integrity? How does the level of English proficiency affect the quality of the Ukrainian scientific products? Is the course of “Academic Writing” a tribute to fashion or a vital necessity? It also investigates the peculiarities of academic fundamentals required for development of certain knowledge, skills and abilities of the Ukrainian language academic writing. The latter aims not only to promote academic integrity by following ethical principles and statutory rules that should guide the participants of the education process while studying, training and conducting research activities, but also to represent the concepts, strategies and tactics of organizing the processes of academic text creation, analysis and editing.
The Spy Who Never Has to Go Out Into the Cold

At the height of the Cold War, spies were more likely to be required to live secret lives in deep undercover. The same held true for corporate and industry spies. Even though government and industry spies may still be required to go out in the field, the digital and big data ages have offered the relative comfort of executing spy operations within one's home or office, offering challenges to detecting, investigating, and controlling espionage. Due to various network means, spies never have to “go out into the cold” – code for going deep undercover. Some of the network conduits discussed are the dark web and deep web and their roles in making covert operations even more secretive than when spies were required to go in the field. Methods of obtaining sensitive information and disabling computer systems are explored as well, including phishing and various means to deliver malware, presented in layman's terms. Once the stuff of science fiction and Hollywood movies, life is very much imitating art, with examples given from famous cyber espionage cases.
Analyzing of Personalized Recommendation Model of Social Network Users Based on Big Data

Aiming at the problem that the traditional social network user interest personalized recommendation model is affected by noise and human factors, which leads to poor recommendation effect, a social network user interest personalized recommendation model based on big data is designed. Analysis the social network users interested in constructing the theoretical basis of personalized recommendation model, analysis of the recommended model the interaction between the model and the surrounding, partitioning server network deployment module, network structure, operation model design through graphs model to recommend task allocation to the distributed computer c1uster, in order to build the user interest personalized recommendation model, using big data double association rules and data mining technology, obtain users interested in network data, through the recommendation results determine the degree of users interested in recommendations, improve recommendation effect, Through experimental comparison, it can be seen that the accuracy of personalized recommendation using this method can reach a maximum of 98%, and the practicability is strong (Abstract).
Aurora Image Search With a Saliency-Weighted Region Network

On account of the remarkable performance of convolutional neural network (CNN) features for natural image searches, utilizing it for other images collected with the anamorphic lens has become a research hotspot. This article selects the aurora images generated from a circular fisheye lens as a typical example. By considering the imaging principle and geomagnetic information, a saliency-weighted region network (SWRN) is presented and introduced into the Mask R-CNN pipeline. Our SWRN selects salient regions with important semantic information and weights them both hierarchically and spatially. Hence, regions encompassing the search target are strengthened while uninformative regions are discarded, which benefits the suppression of background interference and reduction of computational complexity. In practice, by aggregating the outputs of SWRN with post-processing, a compact CNN feature is generated to represent the aurora image. Large-scale aurora image search experiments are conducted, and the results prove that our method performs better than the state-of-the-art methods on both accuracy and efficiency.
Autonomous Swimmers Tracking Algorithm Based on Kalman Filter and CamShift

The visual system is the most important sensory organ of human beings. Most of the external information obtained by human beings comes from vision. With the continuous exploration of the natural world by human beings, people always hope to realize the function of human perception through some digital machine and automatically acquire the information of the outside world. The traditional CamShift algorithm needs to manually locate the target during tracking, and it can be used in complex backgrounds such as color interference and occlusion. In this paper, a comfortable and autonomous swimmer tracking algorithm based on Kalman filter with CamShift is proposed. First, we use the Canny edge detection and inter-frame difference method to segment the entire region of the moving swimmer and then use the extracted target region to initialize the CamShift algorithm. The modified search window supports the automatic tracking of the swimmer. When there is similar color interference in the background, or the swimmer is severely impeded, an improved algorithm combined the Kalman filter with the CamShift algorithm for tracking the target accurately. Experimental results confirmed that the enhanced algorithm significantly improves the outcomes and it can still follow the object effectively and steadily under severe occlusion, noisy environment and color interference.
New Paradigm for Self-embedding Image Watermarking with Poisson Equation

In this paper, we propose a new self-embedding image watermarking scheme based on reference sharing and Poisson equation. With Laplacian operator, the relationship of each pixel and its neighborhood in original image is established and can be converted to compression bits. Then, after scrambling, compression bits are interleaved through the reference sharing mechanism, which can introduce more redundancy into the reference bits to be embedded for future content recovery. Thus, the relationship between each compression bit and each reference bit is constructed so that the recoverable area for tampered image can be increased effectively. Tampered contents can be recovered with the Laplacian values of tampered blocks and the boundary values around tampered blocks based on tampering localization and Poisson equation solver. Experimental results demonstrate the effectiveness of the proposed scheme.
A Systematic Literature Review On Digital Evidence Admissibility: Methodologies, Challenges and Research Directions

Admissibility of Evidence is the eligibility of particular pieces of evidence for inception as part of the evidence in a case. Admissibility means the character or quality to be accepted and allowed to be presented or introduced as evidence in court. To be admissible means capable of being legally admitted or allowable or permissible as evidence or worthy of gaining entry or being admitted. This study focus on carrying out a systematic literature review and meta-analysis on Digital Evidence Admissibility. The methodology employed in this study was the querying of four academic database resources systematically and fundamentally identifying kinds of literature related to digital evidence admissibility through identification, screening, eligibility and inclusion criteria. The advantage of this study is revealing the gap and trends in digital evidence admissibility as the article published between the period of 2015 through 2018 are relative with the following variation 64%, 21% 7%, for the following respectively, IEEE, Science Direct, ACM Digital library, as well as Research Gate. While at the period under review, 2019 is yet to record publication in the field of research in Digital Evidence Admissibility. The period under review witnessed a low academic publication in the field of Digital Evidence admissibility. This research will aid in projecting future research in the aforementioned research field.
Self-Stabilizing Manoeuvre Negotiation: The Case of Virtual Traffic Lights

The vision of automated driving promises to have safer and more cost-efficient transport systems. Automated driving systems have to demonstrate high levels of dependability and affordability. Recent advances of new communication technologies, e.g., 5G, allow significant cost reduction of timely shared sensory information. However, the design of fault-tolerant automated driving systems remains an open challenge. This work considers the design of automated driving systems through the lenses of self-stabilization-a very strong notion of fault-tolerance. Our self-stabilizing algorithms guarantee, within a bounded period, recovery from a broad fault model and arbitrary state corruption. After this recovery period, our algorithms provide safe maneuver execution despite the presence of failures, such as unbounded periods of packet loss and timing failures as well as inaccurate sensory information and malicious behavior. We evaluate the proposed algorithms through a rigorous correctness proof and a worst-case analysis as well as a prototype that focuses on an intersection crossing protocol. We validate our prototype via computer simulations and a testbed implementation. Our preliminary results show a reduction in the number of vehicle collisions and dangerous situations.
A Map Accessibility Analysis Algorithm for Mobile Robot Navigation in Outdoor Environment

Autonomous mobile robots have been widely used to perform specific tasks in recent decades. Map accessibility analysis is necessary for those robots to achieve safe and efficient path planning and navigation, especially in outdoor environment where the terrain can be uneven. Researchers have done sufficient works on the influences of different factors. However, integrating these factors is still a great challenge since the information of some constraints are only needed for specific robot tasks. In this paper, we firstly build a Three Dimensional (3D) Occupancy Grid Map (OctoMap) using a 3D Lidar and encoder-based odometer, and then extract different factors from the OctoMap and real-time lidar data. In order to analyze the relationship between map accessibility and variety of constraints, we design an open system and propose a map accessibility analysis algorithm to integrate different factors which have influence on mobile robot navigation. The system and algorithm are demonstrated to be efficient via experiments.
Research on Pavement recognition algorithm Based on Mobile device

With the development of information technology, the computing power of mobile devices continues to increase, and users can use some computationally complex programs on mobile devices. At present, research related to pavement recognition is mainly in the field of unmanned vehicles. The algorithm proposed in this paper is aimed at the pavement recognition which based on mobile devices. This paper presents a method for identifying pedestrian paths in green spaces. The method uses image filtering and morphological processing to optimize the calculation amount of the image which reduce the operation time of the single image, and classify the main color content of the image. And then process the image in the HSV color space. Finally, we propose a two-dimensional matrix template for extracting the pavement edge information of the processed image. Experimental results show that this method can quickly process images, and effectively identify the pavement edge and pavement area of the target scene, enabling the algorithm to be applied to real-time pavement recognition of mobile devices. The results obtained by this method can be used in mobile devices to provide functions such as road recognition or augmented reality navigation.
Comparison of English and Chinese Speech Recognition Using High-Density Electromyography

Speaking different languages requires different ways of pronunciation, and the muscular activities associated with phonation show different articulation styles. Therefore, clarifying the contributions of the articulatory muscles in different regions, such as the face and neck, is helpful for automatic speech recognition. However, it remains unclear how the articulatory muscles at different positions affect the classification accuracies of speech recognition across different languages. In this study, the technique of high-density surface electromyography (HD sEMG) was proposed to investigate the role of different articulatory muscles in classifying English and Chinese speaking tasks, respectively. The HD sEMG signals were recorded by 120 electrodes evenly placed on the facial and neck muscles across six subjects while they were speaking five English and Chinese daily words. Four time-domain features were extracted from sEMG recordings and used to construct a linear-discriminant-analysis classifier for speech recognition. The results showed that the classification accuracies of using neck sEMG were higher than that of using facial sEMG in both English and Chinese recognition tasks. The accuracies for Chinese speaking tasks were significantly higher than that for English when using facial sEMG only. Moreover, there was no significant difference in accuracies between the two types of languages when using neck sEMG. This study might provide useful information about the contributions of different articulatory muscles, and pave the way for automatic speech recognition across different languages for patients with dysarthria.
Automatic high speed photography based microcontroller

Abstract High-Speed Photography is a technique to capture a moment that moves very quickly. This technique is commonly used by photographers and scientists for research purposes, especially in the fields of physics, chemistry, and metallurgy. To capture the moment that moves quickly the photographer must squeeze the camera manually, then the camera will tend to shake, and the picture is not taken correctly. Also, there is also a delay between squeezing the camera with the shutter movement that adds error factor. In this study designed a high-speed automatic photography tool using a sound sensor that can freeze objects automatically when the sound is heard. To design and make this High-Speed Photography tool automatically use Microcontroller and FC-04 Voice Sound. The results of the overall test conducted to test all the components contained in the tool High-speed automatic photography. The first test scenario is to put the tool ± 50 cm will work optimally, the second test scenario is the sound sensor response when the balloon object to distinguish the object to photograph. This tool is capable of taking pictures automatically when the sound is heard.
Develop the Framework Conception for Hybrid Indoor Navigation for Monitoring inside Building using Quadcopter

Building security is crucial, but guards and CCTV may be inadequate for monitoring all areas. A quadcopter (drone) with manual and autonomous control was used in a trial mission in this project. Generally, all drones can stream live video and take photos. They can also be adapted to assist better decision-making in emergencies that occur inside a building. In this paper, we show how to improve a quadcopter's ability to fly indoors, detect obstacles and react appropriately. This paper represents a new conceptual framework of hybrid indoor navigation ontology that analyzes a regular indoor route, including detection and avoidance of obstacles for the auto-pilot. An experiment with the system demonstrates improvements that occur in building surveillance and maintaining real-time situational awareness. The immediate objective is to show that the drone can serve as a reliable tool in security operations in a building environment.
Neural network‐based image quality comparator without collecting the human score for training

Emulating human behaviours in automated image quality assessment (IQA) enables a comparator framework to remove the differences in human bias naturally. Based on the observation of the practical applications of IQA, this study focuses on similar-content image quality comparison based on a new image quality comparator (IQC). Outstanding proven IQAs can be utilised in this comparator to achieve a new non-linear combination strategy to boost the IQAs' performance in image quality comparison. For both input images to be compared, proven IQAs are utilised to obtain nine features from each image, yielding 18 total features. Then, a four-layer comparison network conducts a classification task to indicate which input image has better quality. In the training phase, the commonly used human scores as training labels are replaced with pairwise comparison results that are automatically generated from assigned distortion level differences. By not utilising human score in training phase, this IQC shows two advantages: (i) it removes huge labor and time cost to collect the human scores and (ii) it solves the problem of over-fitting benefiting from simplicity of creating a large image training dataset. Furthermore, the experimental tests and cross-dataset validation comparison tests demonstrate its impressive performance.
An Approach of Segmentation Method Using Deep Learning for CT Medical Images

In the last few years the filed of deep learning and in general machine learning in medical imaging has become the state-of-the-art in terms of medical image analysis and processing. In our experimental work, we try to provide a high-level approach of fully connected deep neural networks applied in medical image segmentation (lung CT scans), by using Deep Learning GPU Training System (DIGITS) avoiding rigorous mathematical formalism of neural networks. For this purpose using the concept of solving problems by learning from experiences is necessary. The goal of our experimental work is to design an analysis of particular methods in lung CT scans segmentation by using the one of the most used architecture in the filed deep learning for biomedical image segmentation techniques, U-NET architecture. This will lead to obtain in the back-end a mathematical model with the purpose of training to obtain useful results when fed input data and will demonstrate how to train a Fully Convolutional Network (FCN) model like U-NET using segmentation techniques for lung tumor tissues received from Computed Tomographic (CT) device on abdominal images using GPU processing with Nvidia DIGITS.
Encryption Before Compression Coding Scheme for JPEG Image Compression Standard

In this paper we present a new joint encryption and compression coding scheme of natural images which is intended for the use in conjunction with a popular JPEG image compression standard. The encryption is performed prior to compression step and is carried out using fast, parametrized with a private key, linear transformations which do not alter statistical characteristics of the input image data, what enables JPEG algorithm to maintain its full compression capabilities. The work also includes a mathematical model of the proposed scheme which allows for theoretical analysis of the impact of the image encryption step on the compression process. The presented experimental results indicate that the reconstructed images' qualities at a given compression ratios are comparable to those obtained for the JPEG standard without the encryption step.
Stacked Sparse Auto-Encoder for Deep Clustering

Deep clustering attempts to capture the feature representation that benefits the clustering issue for inputs. Although the existing deep clustering methods have achieved encouraging performance in many research fields, there still presents some shortcomings, such as the lack of consideration of local structure preservation and sparse characteristics of input data. To this end, we propose the deep-stacked sparse embedded clustering method in this paper, which considers both the preservation of local structure and sparse property of inputs. The proposed network is trained to capture the feature representation for input data by the guidance of clustering loss and reconstruction loss, where the reconstruction loss prevents the corruption of feature space and guarantees the local structure preservation. Besides, some sparse parameters are added to the encoder to avoid learning of meaningless features. Through simultaneously minimizing the reconstruction loss and cluster loss, the proposed network can jointly learn the clustering oriented feature and optimize the assignment of cluster labels. Then we conduct amounts of comparative experiments, which consist of six clustering methods and four publicly available data sets. Eventually, the clustering accuracy, adjusted rand index and normalized mutual information are utilized as three evaluation metrics to provide a comparison. Comprehensive experiments validate the effectiveness of introducing sparse property and preserving local structure in our method.