Reviewerly/exHarmony huggingface dataset:
Optimization Method of Node Location Accuracy Based on Artificial Intelligence Technology

Abstract Wireless sensor networks(WSN) are one of the hot topics in current information technology research. With the diversification of wireless sensor network applications and the gradual maturity of node positioning technology for static networks, mobile node positioning technology. In order to find a method for optimizing the positioning accuracy of wireless network nodes based on artificial intelligence technology and improve the positioning accuracy, a large-scale simulation experiment is performed in this paper. After constructing a probabilistic model and conducting a large number of simulation experiments, a series of random variable samples are obtained. After statistical processing of these simulation samples, a solution to the problem is found accordingly. By comparing the average positioning error with the ML algorithm, it is found that the SA-L algorithm using artificial intelligence method has less dependence on the density of beacon nodes, improves the accuracy of network node positioning, and is more suitable for node positioning of WSN. Research shows that artificial intelligence technology can improve the accuracy of network node positioning and provide a lot of reference for the development of wireless networks.
FORMATION OF SELF-CONTROL AS A KEY SUCCESS FACTOR IN LIFELONG LEARNING

The strategy of lifelong learning is based on the idea of a constantly ongoing process of personal development. The Don State Technical University, Russia, has a unique experience of lifelong learning as it provides education to the students of all ages. The university has developed a wide range of educational programs for secondary schooling, higher education and post graduate programs, educational programs for elderly people and many others. Lifelong learning comprises both studying at the educational institutions of different levels and self-learning. The success of this process is impossible without some personal qualities such as self-control, responsibility, autonomy. The authors consider fostering these qualities to be an important task of education at different levels among the students of different ages. The objective of the paper is to present the techniques and methods of self-control formation among students which were developed and successfully applied at the Don State Technical University. The theoretical basis of our research is a psychological theory of personality. The technique of self-control formation is based on step by step replacement of teachers’ control over the learners’ progress by mutual learners’ – teachers’ control and later by learners’ self-control over the progress in the learning process which the authors would like to share. Control takes the form of mutual control in a situation of communication and interpersonal interaction of learners and teachers. Self-control is a measure of responsibility of each participant of educational process. One of the necessary conditions of the lifelong education is the involvement of students in the evaluation of their educational activities on the basis of cognitive and behavioral self-control. The pedagogical conditions necessary to foster students’ self-control are: the teacher involvement in the process; formation of students’ self-assessment skills; students’ involvement in various forms of mutual assessment; the formation of students’ evaluative abilities; training and methodological support of the process of learning self-control.
Accuracy Evaluations of Video Anomaly Detection Using Human Pose Estimation

Surveillance cameras are commonly used for security purpose. However, most of them are used for verification after incidents happen. In this paper, we propose a method for proactive video surveillance system using human pose estimation. Our method estimates anomaly scores of human actions using the video descriptors (human pose and bounding box) extracted by pose estimation and tracking methods. We estimate human poses, apply PCA, estimate GMM parameters, and finally calculate anomaly scores based on the GMM. We evaluate our method and compare with higher-level recognition-based method. Experimental results demonstrate effectiveness of human pose for video anomaly detection.
An Adaptive Quantization Based PVC Scheme for HEVC

In order to achieve highly compact representation for videos, we propose an adaptive quantization based perceptual video coding (PVC) scheme in this paper. Because human only perceive the limited discrete-scale quality levels, the perceptual quantization is transformed into the problem of how to determine the maximum quantization parameter (Qp) under the same perceptual quality level. So, the relationship between perceptual quality level and quantization parameter is analyzed with the statistical way in this paper. The frame-level Qp value for each quality level is determined based on the maximum probability criterion. Then, the just noticeable distortion is estimated to guide the Qp adjustment in the coding unit level (CU-level). In summary, the perceptual quantization is achieved in both the frame level and the CU level according to characteristics of human visual system (HVS). Experimental results show that the proposed PVC scheme achieves substantial bitrate reduction with better subjective and objective quality in comparison with other PVC schemes.
Infrared Video Enhancement Using Contrast Limited Adaptive Histogram Equalization and Fuzzy Logic

Infrared image enhancement is a challenging task due to several factors such as low dynamic range, noise and non-uniformity effect. The non-uniformity is a time-dependent noise that appears owing to the lack of sensor equalization. This paper presents two proposed approaches for infrared video enhancement. The first proposed approach depends on histogram matching. The second one depends on contrast limited adaptive histogram equalization (CLAHE) and fuzzy logic. The performance metrics of average gradient, entropy, contrast improvement factor and Sobel edge magnitude are used for evaluating the obtained results.
Censorship-resistant web annotations based on ethereum and IPFS

Flooded by the propagation of false or biased news in the Web, people tend to resort to social networks to read posts from reliable sources, exchange commentaries with trustworthy parties, access first-hand content, or cross-check information that appears in news outlets. However, platform providers like Facebook or Twitter can ultimately decide about the contents exposed to each user. Anecdotal evidence suggests that such platform providers are prone to pressure by political or economical agents, and may be ideologically driven to hide messages or block certain users [2, 12] thereby impairing users' ability to freely access rightful information.
Comparative Performance Evaluation of Popular Virtual Private Servers

Virtual Private Server (VPS) enables user access to an operating system instance with a fine-grained control of private software and hardware resources. Many various factors can affect VPS performance and they primarily include physical server hardware specifications, installed operating system, virtualization layer, and the underlying network infrastructure. Therefore, it is very important to properly select a VPS host that meets users, applications and services resource and performance requirements. This paper presents a performance evaluation of three popular VPS hosts; namely Digital Ocean, Linode and VULTR. Performance measurement was conducted under the same controlled conditions for all three VPS hosts using a popular benchmark application for Unix operating systems - UnixBench. Three performance evaluation experiments with a focus on examining and studying key performance metrics which include CPU scheduling, memory management, hard disk drive management and Unix operating system task scheduling, were conducted. Performance easurement results show that VULTR achieves the best results under most of the tests in the first two experiments making it the best choice for low demand users, while DigitalOcean achieves the best results in the third experiment making it the best solution for high demand users who are looking for a high performance VPS.
Fractional Lévy flight bat algorithm for global optimisation

A well-known metaheuristic is the bat algorithm (BA), which consists of an iterative learning process inspired by bats echolocation behaviour in searching for prays. Basically, the BA uses a predefined number of bats that collectively move on the search space to find the global optimum. This article proposes the fractional Lévy flight bat algorithm (FLFBA), which is an improved version of the classical BA. In the FLFBA the velocity is updated through fractional calculus and a local search procedure that uses a random walk based on Lévy distribution. Such modifications enhance the ability of the algorithm to escape from local optimal values. The FLFBA has been tested using several well-known benchmark functions and its convergence is also compared with other evolutionary algorithms from the state-of-the-art. The results indicate that the FLFBA provided in several cases better performance in comparison to the selected evolutionary algorithms.
Experimental Studies on the Impact of Data Sampling with Severely Imbalanced Big Data

This introduction presents an overview of the key concepts discussed in the subsequent chapters of this book. The book focuses on sampling to reduce the impact of class imbalance on machine learning models. It demonstrates that classification performance across several imbalanced big datasets across different application domains can be significantly improved using Random Undersampling without substantially altering the composition of the original data. The book provides an overview of related works. It describes the Machine Learning (ML) classification algorithms and libraries, to include the evaluation strategy with validation techniques and performance metrics. The book introduces the datasets and how they were processed, model training, and performance evaluation. It involves a real-world Medicare fraud problem, with severe class imbalance. To ease the process of using ML, engineers build the algorithms within software modules or packages, making sure that they work reliably, quickly, and at-scale.
Event-based summarization of news articles

In recent years, with the increase of available digital information on the Web, the time needed to find relevant information is also increased. Therefore, to reduce the time spent on searching, research on automatic text summarization has gained importance. The proposed summarization process is based on event extraction methods and is called an event-based extractive single-document summarization. In this method, the important features of event extraction and summarization methods are analyzed and combined together to extract the summaries from single-source news documents. Among the tested features, six features are found to be the most effective in constructing good summaries. The constructed summaries are tested on benchmark Document Understanding Conferences 2001 and 2002 datasets, and the results outperformed most of the other well-known summarization methods.
Sequential Weakly Labeled Multi-Activity Recognition and Location on Wearable Sensors using Recurrent Attention Network

With the popularity and development of the wearable devices such as smartphones, human activity recognition (HAR) based on sensors has become as a key research area in human computer interaction and ubiquitous computing. The emergence of deep learning leads to a recent shift in the research of HAR, which requires massive strictly labeled data. In comparison with video data, activity data recorded from an accelerometer or gyroscope is often more difficult to interpret and segment. Recently, several attention mechanisms are proposed to handle the weakly labeled human activity data, which do not require accurate data annotation. However, these attention-based models can only handle the weakly labeled dataset whose segment includes one labeled activity, as a result it limits efficiency and practicality. In the paper, we proposed a recurrent attention network to handle sequential activity recognition and location tasks. The model can repeatedly perform steps of attention on multiple activities of one segment and each step is corresponding to the current focused activity according to its previous observations. The effectiveness of the recurrent attention model is validated by comparing with a baseline CNN, on the UniMiB-SHAR dataset and a collected sequential weakly labeled multi-activity dataset. The experiment results show that our recurrent attention model not only can perform single activity recognition tasks, but also can recognize and locate sequential weakly labeled multi-activity data. Besides, the recurrent attention can greatly facilitate the process of sensor data accumulation by automatically segmenting the regions of interest.
Rule Interaction Conversion Operations

Different types of interactions between pairs of phonological rules can be converted into one another using three formal operations that we discuss in this article. One of these conversion operations, rule re-ordering (here called swapping), is well-known; another, flipping, is a more recent finding (Hein et al., 2014). We introduce a third conversion operation that we call cropping. Formal relationships among the members of the set of rule interactions, expanded by cropping beyond the classical four (feeding, bleeding, counterfeeding, and counterbleeding) to include four more (mutual bleeding, seeding, counterseeding, and merger), are identified and clarified. We show that these conversion operations exhaustively delimit the set of possible pairwise rule interactions predicted by conjunctive rule ordering (Chomsky &amp; Halle, 1968), and that each interaction is related to each of the others by the application of at most two conversion operations.
Dynamic No-Fly Zone for Drones

Recently, drones have been widely used for a variety of purposes, such as surveillance, journalism, environmental protection, disaster management and various leisure activities. However, due to the noneffective monitoring of drones, the accidents of drones interfere with low-flying aircrafts and civil aviations emerge in an endless stream, resulting in many issues in safety. In this paper, we have designed the Dynamic No-fly Zone, which is based on a sphere centered on the current flight with a radius, to accurately model the no-fly zone of drones. Furthermore, in order to deal with the big data produced by the dynamically changed positions of flights and aircrafts, we have presented a Two-Level Dynamic Alert Zone. On top of it, a machine learning algorithm named Positive Logic Model (PLM) is proposed for the realization of the Dynamic No-fly Zone for drones. Without extra hardware support, our heuristic model and algorithm are able to solve the problem of drones interfere with low-flying aircrafts and civil aviations. Simulation experimental results demonstrate the effectiveness of our heuristic approach.
An Immersive Web Visualization Platform for a Big Data Context in Bosch’s Industry 4.0 Movement

Motivated by challenges that emerged from Complex Event Processing (CEP) in Big Data contexts, an Intelligent Event Broker (IEB) was previously proposed as a CEP system built on flexible and scalable Big Data technologies and techniques, being already applied to Industry 4.0 scenarios. A key feature of the IEB lies in an effective visualization system for meta-monitorization and management. An analysis of previous scientific and technical literature has shown scarcity of proposals or development of such mechanisms. This paper proposes a Web Visualization Platform for managing and monitoring an IEB system based on novel approaches that include interactive exploration techniques, tridimensional visualizations and mixed-reality environments. Being mainly targeted towards Industry 4.0, this paper presents a demonstration case at Bosch Car Multimedia Portugal. Results indicate that significant value can be obtained when the visualization system is applied to decision support scenarios within organizations foreseeing event processing, Big Data, and Industry 4.0 projects.
Max-Plus Generalization of Conway’s Game of Life

We propose a max-plus equation which includes Conway's Game of Life (GoL) as a special case. There are some special solutions to the equation which include and unify those to GoL. Moreover, the multi-value extension of GoL is derived from the equation and the behavior of solutions is discussed.
Recognition of Holoscopic 3D Video Hand Gesture Using Convolutional Neural Networks

The convolutional neural network (CNN) algorithm is one of the efficient techniques to recognize hand gestures. In human–computer interaction, a human gesture is a non-verbal communication mode, as users communicate with a computer via input devices. In this article, 3D micro hand gesture recognition disparity experiments are proposed using CNN. This study includes twelve 3D micro hand motions recorded for three different subjects. The system is validated by an experiment that is implemented on twenty different subjects of different ages. The results are analysed and evaluated based on execution time, training, testing, sensitivity, specificity, positive and negative predictive value, and likelihood. The CNN training results show an accuracy as high as 100%, which present superior performance in all factors. On the other hand, the validation results average about 99% accuracy. The CNN algorithm has proven to be the most accurate classification tool for micro gesture recognition.
An Enhanced MSER Pruning Algorithm for Detection and Localization of Bangla Texts from Scene Images

Text detection and localization have great importance for content based image analysis and text based image indexing. The efficiency of text recognition depends on the efficiency of text localization. So, the main goal of the proposed method is to detect and localize text regions with high accuracy. To achieve this goal, a new and efficient method has been introduced for localization of Bangla text from scene images. In order to improve precision and recall as well as f-measure, Maximally Stable Extremal Region (MSER) based method along with double filtering techniques have been used. As MSER algorithm generates many false positives, we have introduced double filtering method for removing these false positives to increase the f-measure to a great extent. Our proposed method works at three basic levels. Firstly, MSER regions are generated from the input color image by converting it into gray scale image. Secondly, some heuristic features are used to filter out most of the false positives or non-text regions. Lastly, Stroke Width Transform (SWT) based filtering method is used to filter out remaining non-text regions. Remaining components are then grouped into candidate text regions marked by bounding box over each region. As there is no benchmark database for Bangla text, the proposed method is implemented on our own prepared database consisting of 200 scene images of Bangla texts and has got prominent performance. To evaluate the performance of our proposed approach, we have also tested the proposed method on International Conference on Document Analysis and Recognition( ICDAR) 2013 benchmark database and have got a better result than the related existing methods.
"Deep-speare" crafted Shakespearean verse that few readers could distinguish from the real thing

Deep-speare's creation is nonsensical when you read it closely, but it certainly "scans well," as an English teacher would say-its rhythm, rhyme scheme, and the basic grammar of its individual lines all seem fine at first glance. As our research team discovered when we showed our Al's poetry to the world, that's enough to fool quite a lot of people; most readers couldn't distinguish the AI-generated poetry from human-written works.
Robust table recognition for printed document images

&lt;abstract&gt; &lt;p&gt;The recognition and analysis of tables on printed document images is a popular research field of the pattern recognition and image processing. Existing table recognition methods usually require high degree of regularity, and the robustness still needs significant improvement. This paper focuses on a robust table recognition system that mainly consists of three parts: Image preprocessing, cell location based on contour mutual exclusion, and recognition of printed Chinese characters based on deep learning network. A table recognition app has been developed based on these proposed algorithms, which can transform the captured images to editable text in real time. The effectiveness of the table recognition app has been verified by testing a dataset of 105 images. The corresponding test results show that it could well identify high-quality tables, and the recognition rate of low-quality tables with distortion and blur reaches 81%, which is considerably higher than those of the existing methods. The work in this paper could give insights into the application of the table recognition and analysis algorithms.&lt;/p&gt; &lt;/abstract&gt;
Training Machine Learning Network on Adreno Mobile GPUs Using OpenCL

Training on the edge has becoming an interesting topic recently, largely thanks to the inherent drawbacks in cloud-based training methods for mobile users. Traditional methods typically require users to upload their data into cloud, which raises concerns on privacy and data compromise. Users may prefer to have customized models rather than general models from cloud. In addition, it is a waste of resource not to use the computing power of mobile devices.
BULLY IDENTIFICATION WITH MACHINE LEARNING ALGORITHMS

The purpose of this paper is to identify the bullies via informed surveys, using data from students of colleges and schools and take the results to concerned authority or guardian and list out ways to eradicate it. This paper adopts data mining techniques of the concerned survey results and convert into knowledge. Following a five step process of Data Selection, Pre- Processing/Cleaning, Transformation, Data Mining and Inter- pretation/Evaluation. Along with this the paper utilizes three unique approaches, Internal Labelling, Synthetic Labelling and Data Programming. To identify these data patterns effectively, incorporation of data validation and classification using suitable Machine Learning algorithms is utilized.
Human–Machine Collaboration for Democratizing Data Science

Everybody wants to analyse their data, but only few posses the data science expertise to do this. Motivated by this observation, we introduce a novel framework and system VisualSynth for human-machine collaboration in data science. Its aim is to democratize data science by allowing users to interact with standard spreadsheet software in order to perform and automate various data analysis tasks ranging from data wrangling, data selection, clustering, constraint learning, predictive modeling and auto-completion. VisualSynth relies on the user providing colored sketches, i.e., coloring parts of the spreadsheet, to partially specify data science tasks, which are then determined and executed using artificial intelligence techniques.