Reviewerly/exHarmony huggingface dataset:
Fuzzy clustering algorithm modelling of data stream mining based on particle swarm optimization and GA

Fuzzy clustering algorithm modelling of the data stream mining based on particle swarm optimization and GA is designed and implemented in this manuscript. In the intelligent optimization algorithm, the novel particle swarm optimization algorithm does not require any derivation of the solution function, and can overcome the complexity of the traditional optimization algorithm in the calculation and the disadvantages of the remaining optimization. In this regard, the GA model has been applied to enhance the training process of PSO. The data stream is selected as the experimental scenario to validate the model. The fuzzy model with the clustering analysis is combined for the optimal analysis. The robustness of the framework is tested.
Reputation Analysis Based on Weakly-Supervised Bi-LSTM-Attention Network

Due to the emergence of rapid, mass-produced information in the Web2.0 era, a large amount of weakly labeled information (star ratings, etc.) has been widespread. The Weakly-Supervised Deep Embedding (WDE) model is a good choice for utilizing this kind of data. The ratings are treated as weakly-supervised signals for pre-training, fine tuning the whole model with a small amount of manually labeled data. In this research, we proposed to change the original unidirectional transmission into bidirectional in the LSTM layer to capture the semantics in both directions, and an attention mechanism is introduced, which is helpful to capture the important information in the context and improve the accuracy of sentiment classification. Finally, we use TF-IDF and LDA topic models to mine the review topics and extract the consumers’ opinions on different sentiment polarities.
Analog information processing with time-multiplexed optoelectronic systems

We present a systematic approach to the design of an analog implementation of photonic reservoir computing. The scheme builds on the idea that, thanks to time-multiplexing, a single nonlinear node subject to a delayed feedback loop can emulate a network with ring-like topology. We go beyond previous approaches for analog photonic reservoir computers by considering a ow model (continuous time) of the corresponding optoelectronic implementation, instead of the usually considered map limit, as the continuous time approach allows for operating at faster modulations. We focus on the implementation of an analog output layer made of a modulator and a second order filter that makes any digital post-processing unnecessary. Numerical simulations of the system show that the suggested analog design of the analog output layer is robust towards potential experimental deviations such as time jitter. The combination of the optoelectronic implementation with an analog output layer allows for high-speed information processing.
Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons

We identify fragile and robust neurons of deep learning architectures using nodal dropouts of the first convolutional layer. Using an adversarial targeting algorithm, we correlate these neurons with the distribution of adversarial attacks on the network. Adversarial robustness of neural networks has gained significant attention in recent times and highlights intrinsic weaknesses of deep learning networks against carefully constructed distortion applied to input images. In this paper, we evaluate the robustness of state-of-the-art image classification models trained on the MNIST and CIFAR10 datasets against the fast gradient sign method attack, a simple yet effective method of deceiving neural networks. Our method identifies the specific neurons of a network that are most affected by the adversarial attack being applied. We, therefore, propose to make fragile neurons more robust against these attacks by compressing features within robust neurons and amplifying the fragile neurons proportionally.
An Evolutionary Framework for Real-Time Fraudulent Credit Detection

Fraud has been a worldwide issue that is facing the major economies of the world. Within an economical system, undetected and unpunished fraudulent activities can erode the public trust in law enforcement institutions and even incentivize more fraud. Therefore, detection of fraudulent activities and prosecution of responsible entities is of utmost importance for financial regulatory bodies around the globe. Of the challenges rising with this task is the scarcity of detection resources (auditors) and the fraudsters constantly adapting to the new circumstances of the market. To address these issues, this paper proposes an evolutionary framework for credit fraud detection with the ability to incorporate (and adapt to) the incoming data in real-time. The goal of the framework is to identify the entities with high a risk of fraud for efficient targeting of the scarce resources. The data that is generated as a result of the audits are fed into the framework for further training.
Study Cases to Test the Optimization Performed in the Hybrid Model

In this chapterHybrid model, each of the optimizationsOptimization made to the proposed modelModel is described and comparisons of the different algorithms used for the mentioned optimizations are made to analyze the performance of each one and determine with which one a better result is obtained, and these experiments are organized as study cases.
Research on Edge Computing of Automatic Control System of Unattended Intelligent Manufacturing Equipment

In order to solve the problem that unattended substations are difficult to monitor in real time due to the large number of points, this paper proposes an intelligent classification edge computing algorithm for real-time monitoring of inbound personnel. The paper uses the continuous-time Markov algorithm to complete the intelligent classification of the personnel entering the station: record the name and time of the entry for the known personnel, and perform the alarm function and other prescribed actions for the stranger. Experimental results in practical applications show that adjusting the hyperparameters of the algorithm will achieve different sensitivity and recognition rates. After fine- tuning the hyperparameters, the accuracy of the algorithm reaches about 90%. The monitoring platform developed based on this algorithm has been deployed on smart terminals, relying on edge computing technology to realize automatic identification of unattended substation personnel entering the station, and achieved expected results in production practice.
Multi-Task Network and Optimization for Face Detection and Attribute Analysis

More and more application scenarios require algorithms to be able to detect human faces while predicting facial attributes such as gender and age. However, the existing face detection and facial attribute analysis are generally been solved as separate problems. At the meantime, how different tasks influence each other and how to balance and optimize multiple tasks still need to be further studied. Therefore, we design a novel multi-task network to jointly detect faces and predict facial attributes. Furthermore, we propose an optimization method based on noise estimation to adaptively tune the multi-task loss weights. Experimental results on CelebA dataset show that our method achieves great performance in both accuracy and speed.
Crystal size and shape distributions plagioclase and determining of crystal residence time in the Chah-Musa intrusion (South Shahrood)

Crystal size and shape distributions plagioclase and determining of crystal residence time in the Chah-Musa intrusion (South Shahrood)
Video—Geographic Scene Fusion Expression Based on Eye Movement Data

Video-geographic scene fusion is the development trend in surveillance video systems. How to analyze the influence of various factors on the fusion expression effect is of great significance. In this paper, we select video compression state, video scene fusion expression and viewer features as the impact factors of fusion expression visualization. And the influence of above factors was analyzed systematically by eye movement experiment. Experimental results show that these three factors have a certain impact on the video-geographic scene fusion expression effect. The expression effect of uncompressed video is better than that of compressed video. No significant difference exists between the embedded and foreground projection, but a significant difference exists between the correlation and the previous two expressions. In terms of viewer features, we take gender factors into account in this paper. Under the condition of normal corrected vision and the same age, the accuracy of information received by women is higher than that of men based on the gender analysis of the observer.
An Inexpensive Design of Agent’s Behavior During a "Picking Task" in a Simulated 2-D Virtual Game-Like Environment

Agent’s autonomous and intelligent behavior represents an important topic in the field of Artificial Intelligence. One could even say that it is the core part of the AI field. There are different real-world and simulation scenarios that can be used for exploring of agent’s capabilities. In recent years, computer games, virtual game-like environments, and virtual worlds in general, established themselves not only as an important part of consumer electronics, but also as an excellent testbed for developing of different kinds of agents. At the same time, development of agent technology significantly enhances these virtual environments. In this paper, we will demonstrate how a computationally inexpensive design of agent’s behavior during the "picking task" scenario in such virtual environment, can lead to a satisfying performance.
Wearable Fitness Devices: An Investigation into Co-constructed Meaning of Use

This article investigates practical uses of Wear Fitness Devices (WFDs) and extends literature to include a co-constructed process. Previous research has looked at various aspects of the devices. Yet, this article explores WFD at the crossroads of communication and sport. Researchers conducted 43 in-depth interviews about their WFD usage patterns. The researchers utilize the Coordinated Management of Meaning to understand the themes that emerged. Researchers also discuss the co-construction of understanding that occurred during interviews that revealed the concept of mindless usage. Researchers also demonstrate the relevance of Uses and Gratifications in uncovering five key themes: “features and benefits,” “health and fitness,” “motivation,” “communication,” and “life changes.” The interview process reveals a shift in understanding the meaning of WFD use and reveals themes that demonstrate practical usage. Suggestions for future research on WFD are also provided.
The Ico Paradox: Transactions Costs, Token Velocity, and Token Value

Blockchain technology offers firms a novel method of raising capital, via so-called Initial Coin Offerings (ICOs). In the most novel form of an ICO, a firm creates digital assets called “utility tokens” that are tracked on a blockchain-based ledger; requires that its product be purchased only with those tokens; and then raises capital by selling these tokens to investors prior to creating any saleable product. We point out a fundamental paradox with the use of ICOs involving utility tokens. Requiring the use of utility tokens to purchase the firm's product increases the cost of that product by an amount proportional to the cost of running the blockchain that tracks the utility token. In order to increase product revenue—and thus capital raised via an ICO—the firm will want to reduce these blockchain-operating costs. Doing so, however, increases the number of utility-token transactions that take place in any time interval, i.e., increases token velocity and thus the effective supply of tokens. By Fisher's equation, this lowers the dollar value of tokens and thus the amount investors are willing to pay for them. This paradox limits the value of utility-token ICOs. We discuss alternatives to and variations of utility tokens that can mitigate the conundrum.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
The Use of Social Media for Education and Digital Competence Enhancement Among Youth in Serbia

Intensive development of information technologies has caused changes in all social segments, including education. The need to revise educational practices arose as a consequence of the fact that there is a growing need for individuals who possess advanced ICT skills in the global labour market. Given that most educational practices in Serbia are still based on traditional learning methods, the need to bridge the gap between practical skills and theoretical knowledge is a constant challenge. Greater success in the implementation of e-education in traditional educational practices in Serbia could be achieved by using social media as software that most young people perceive as a natural environment. Through a review of the existing body of knowledge, this paper considers the benefits of using social media in educational practices. In order to gain insight into the attitudes of students in Serbia regarding the application of social media in education, an empirical research was conducted. Based on the obtained results, conclusions and recommendations for future research were presented.
Embodied Composition: Composing the Body with Sound

Abstract Embodied composition is the practice of organizing sound in relation to the body and its internal/external experiences. It includes the intuitive and corporeal capacity to create, remember and respond to the environment. This creative practice manifests in the use of voice, gesture and the creative state. While many composers work with embodied composition, a clear definition is lacking. Toward this end, the author offers a perspective from her own creative practice as research. Her definition is illustrated with examples from Meredith Monk, Pamela Z and Cassandra Miller.
All For Naught: An Empirical Examination of the Impact of Breach Notification Laws

From 2003-2018, 50 states and the District of Columbia enacted breach notification laws (BNLs) mandating that firms notify affected persons and undertake mitigation when a data breach occurs. BNLs were supposed to decrease data breaches and develop a market for data privacy where firms could strike their preferred balance between data security quality and cost. We find no evidence for either supposition. Results from two-way difference-in-difference analyses indicate no decrease in data breach incident counts or magnitudes following BNL enactment. Results also indicate no longer-term decrease in data misuse after breaches. These non-effects persist for different firms, time-periods, data-breach types, and BNL types. Apparently inconsistent notification standards, insufficient penalties for untimely notification, and inadequate information dissemination to the public explain BNL ineffectiveness. An alternative federal regime could address these shortcomings and let a national BNL achieve goals state BNLs failed to meet. (142 words)This version: March 2022
Computational program to evaluate local defects type Chalmers Test on a reflective optical surface

A computational tool is developed to measure the local deformations in optical surfaces from the interference patterns obtained by the Chalmers test principle and from the analysis of a reflective optical surface using a commercial Fizeau interferometer of the ZYGO. The tests were made on a concave spherical mirror with a radius of curvature of 60 cm and a diameter of 13 cm. To obtain the measurements of local deformations, a computational tool proposed for the localization of dark fringes is used by evaluating the maximum and minimum of the image obtained in the interference patterns. The results obtained show that the computational tool allows locating fringes within an interference pattern, allowing faster inter-fringe measurements and assigning an error on the surface in terms of wavelength.
A Benchmark Dataset for Segmenting Liver, Vasculature and Lesions from Large-scale Computed Tomography Data

How to build a high-performance liver-related computer assisted diagnosis system is an open question of great interest. However, the performance of the state-of-art algorithm is always limited by the amount of data and the quality of the label. To address this problem, we propose the biggest treatment-oriented liver cancer dataset for liver surgery and treatment planning. This dataset provides 216 cases (total about 268K frames) scanned images in contrast-enhanced computed tomography (CT). We labeled all the CT images with the liver, liver vasculature, and liver tumor segmentation ground truth for train and tune segmentation algorithms in advance. Based on that, we evaluate several recent and state-of-the-art segmentation algorithms, including 7 deep learning methods, on CT sequences. All results are compared to reference segmentations five error metrics that highlight different aspects of segmentation accuracy. In general, compared with previous datasets, our dataset is really a challenging dataset. To our knowledge, the proposed dataset and benchmark allow for the first time systematic exploration of such issues, and will be made available to allow for further research in this field.
A Survey on the Use of Personalized Model-Based Search Engine

Mostly with growth of the information sector, in particular the World wide web and the mobile Internet, the volume of information that we’ll have to contend with is rapidly increasing. To a certain degree, users could get the information they need from the Internet. And in context of these vast availability and proliferation of information, users need an efficient way of finding only important and interesting information. How to optimize usability during information search process is still not addressed. Searching was the key feature of a conventional search engine. There has been insufficient customization in search engines to be used by searchers during the information extraction process. This article thus analysed and discussed the personalized recommendations of searches using a tailored framework that supports search engines. We describe a comprehensive review of the study on search engine customization for web-based queries. The study is partially systematic and, also at end of the study, we discuss the difficulties of personalizing search engines.
Reliable detection of pulse arrival time for partial discharge location in HV cable based on improved Allen-Bear algorithm

In multi-sensor data based partial discharge (PD) source location in high voltage (HV) cable, poor reliability of detection of pulse arrival time against noises, waveform distortion, amplitude attenuation is the major barriers to practical application. In this paper, an improved Allen-Bear PD pulse detection algorithm, which combines the advantage of the characteristic functions of the two time-window energy ratio based detection algorithms, is proposed for robust PD location. The most critical contribution of proposed method is the extension of frequency domain component compared to the characteristic functions of the original algorithm. Simulation and physical experiment results demonstrate that the proposed method not only ensures the detection accuracy of the PD pulse arrival time, but also improves the reliability under harsh practical scenarios. It performs significantly better than the existing detection algorithm in terms of anti-noise, anti-waveform distortion, time window length variation, etc. It has significant value for practical applications.
Logistic and Tent Map Encrypted Image Steganography in Transformation Domain using DWT-LSB Technique

The data confidentiality in an image is predominant in digital-world and outstretching the importance. Steganography and Cryptography are generally used for securing information. The security level is enhanced by encrypting the secret image using dual chaotic system models to generate random encryption sequences using Logistic and Tent map in proposed algorithm. The encrypted image is inserted into another image in transformation domain using DWT-LSB steganography techniques. Proposed method is robust and providing high information security with a good SSIM and PSNR for encryption process and embedding process respectively.
Multi-Label Classification of Research Papers Using Multi-Label K-Nearest Neighbour Algorithm

Abstract With the frequent interaction and cooperation between different disciplines in recent years, the number of research papers associated with multiple subjects increased. Correspondingly, some of the existing literatures belong to a single discipline, while others may simultaneously involve more than 2 subjects. At this time, the traditional single-label text classification is not conducive to people obtaining comprehensive and cutting-edge research papers in real life. Thus, it’s of great importance to conduct a multi-label classification of research papers effectively. This paper tests the performance of multi-label learning tasks with text data obtained from the Kaggle website. Firstly, lemmatization and Term Frequency-Inverse Document Frequency (TF-IDF) are used for feature extraction in the pre-processing part. The critical information of text content is statistically analysed, and text content is converted into numerical and high-dimensional vector space. As the traditional single-label classification algorithm is not suitable for the above problem, this paper adopts the Multi-Label K-Nearest Neighbour (ML-KNN) algorithm framework for classification. Experimental results report that the ML-KNN algorithm has achieved better results in multi-label text classification problems than a traditional multi-label algorithm, which proves the effectiveness of the ML-KNN algorithm for text data prediction with multiple subjects. Moreover, the work in this paper is analysed and summarized.
Visual Rotational Constraints of General UAS Configurations

This paper introduces an algorithm for determining the relative rotational limits of a camera along a given axis or set of axes for maintaining a known target within a defined visual field-of-view. An additional algorithm is given for identifying the axis and rotation from a set available rotational degrees of freedom resulting in the shortest transition of target visibility. Bound outputs are shown for a generic unmanned aircraft system (UAS) equipped with a two-axis gimbal in simulation. A variety of illustrative examples are provided for applications involving a gimballed UAS.
Experience with the usage of virtual reality worlds about natural history in Slovenia

The virtual reality worlds on the web obviously present in many parameters a better tool for education as classical paper learning software. In its extreme, the electronic paper textbook can be included as a pdf file. The interactivity and ease to transport, copy and adapt such material make it very practical. In addition it is more durable than paper because it can be easily reproduced. We produced a software package “Cell-Tissue-Body” teaching biology in the secondary and primary schools in Slovenia. The Ministry of education supported this project and it is free to obtain. We discuss here first the advantages and disadvantages of the electronical textbooks, then the difficulties when implementing them in our schools and finally our thoughts how to overcome the possible difficulties in future updates of this and other projects.