Reviewerly/exHarmony huggingface dataset:
A Real-Time Fuzzy Motion Planning System for Unmanned Aerial Vehicles in Dynamic 3d Environments

This paper presents a new fuzzy potential system to plan the motion of Unmanned Aerial Vehicles (UAVs) in dynamic 3D space. The system consists of two fuzzy subsystems representing the attractive and repulsive models of virtual forces in 3D space. The attractive model generates the force required to pull the UAV in a smooth and optimized trajectory to land softly on a moving or stationary target. In addition, the repulsive model generates the necessary repulsive force to avoid any static or moving obstacles in the 3D space. The attractive fuzzy inference system takes the relative position and velocity between UAV and target in the x, y, and z directions as inputs and generates the required attractive force in the x, y, and z directions. The repulsive fuzzy inference system takes the relative position between UAV and obstacle in the x, y, and z directions as input. The fuzzy associative memory (FAM) models the inputs and generates the required repulsive force in the x, y, and z directions. As a result, the UAV moves simultaneously under the influence of fuzzy virtual attractive and repulsive forces. Accordingly, it will be able to change both its altitude and projected planner position concurrently and resolve the local minima problem if occurred. On the other hand, many classical models in dynamic environments required several additional inputs, such as the relative position and relative velocity, which increase the requirement of the measurement system to localize the moving objects in the 3D space. Several experiments were performed and discussed to verify the robustness and effectiveness of the proposed motion planner with real-time implementations. The system performance was validated using three robotics platforms (two quadcopter drones and one ground robot). The position and orientation of each robot were defined using a motion capture system with six opti-track cameras. The motion planning system produces an efficient and accurate low-frequency trajectory for a quadcopter drone. The generated trajectory allows the drone to track the ground robot and avoid collision effectively with the second drone.
Import vertical characteristic of rain streak for single image deraining

Recently, deep convolutional neural networks show good effect for single image deraining. These networks always adopt the conventional convolution method to extract features, which may neglect the characteristic of rain streak. A novelty vertical module is proposed to focus on the vertical characteristic of rain streak. Such module uses 1 $$\times X$$ convolution kernel to extract the vertical information of rain streaks and a $$X \times X$$ convolution kernel to keep relative location information. Use this module in the front of deraining network can better detach rain streaks from background. In addition, the contrastive learning is employed to improve the performance of the model. Extensive experimental results demonstrated the superiority of the deraining methods with the proposed methods in comparison with the base ones.
Fiscal Measurement and Oil and Gas Production Market: Increasing Reliability Using Blockchain Technology

The market of oil and gas has many particularities, once it is a natural resource of great value. Due to the fact the resource is owned by nations, it is highly regulated. The volumes traded are high; for this reason, their calculation needs to be performed very carefully, meeting not only the uncertainties and metrological control, but also particularly the tracking of the activities. The technical regulations of measurement adopted by the countries carefully follows many guidelines. The reason is that the established volumes directly affect the calculation of royalties and profit sharing in concession agreements or even a simple ownership shift of the products. Therefore, it is an application with a lot of responsibility, involving a large amount of equipment, software, and execution processes. Therefore, the transfer of data among different entities requires total transparency and security. Blockchain technology, which has been initially developed for the financial market, presents itself as an alternative to ensure reliability, from the sensors in the field to the effective generation of the Monthly Report on the petroleum and natural gas production unit, which is the basic document for determining the remuneration of the owners of the product. This paper presents a technical solution for creating the blockchain validation blocks by the MAC (Media Access Control Address) addressing, which in turn comes from the communication boards of the flow computers and from the Supervisory Stations. There are limitations to extending this solution to the level of field sensors due to the current links of communication, but also because of the way that historical, events and alarm databases of the flow computers are generated. Once these devices exhibit an elevated degree of safety in their operation, the solution herein presented adds a high level of reliability in the fiscal measurement and/or custody transfer.
GPU-accelerated Proximity Graph Approximate Nearest Neighbor Search and Construction

The approximate nearest neighbor (ANN) search in high-dimensional space offers a wide spectrum of applications across many domains such as database, machine learning, multimedia and computer vision. A variety of ANN search algorithms have been proposed in the literature. In recent years, proximity graph-based approaches have attracted considerable attention from both industry and academic settings due to the superior search performance in terms of speed and accuracy. A recent work utilizes a graphics processing unit (GPU) to accelerate the ANN search on proximity graphs. Though significantly reducing the distance computation time by taking advantage of the massive parallelism of GPUs, the algorithm suffers from the high expenses of data structure operations. In this paper, we propose a novel GPU -accelerated algorithm that designs a novel GPU-friendly search framework on proximity graphs to fully exploit the massively parallel processing power of GPUs at key steps of the search. Also, we propose GPU-accelerated proximity graph construction algorithms which can build high-quality representative proximity graphs with efficient parallel implementations. Extensive experiments on benchmark high-dimensional datasets demonstrate the outstanding performance of our proposed algorithms in both ANN search and proximity graph construction.
The New Thoreaus

Fifty years ago, in Wisconsin v. Yoder, the Supreme Court famously indicated that “religion” denotes a communal rather than a purely individual phenomenon. An organized group like the Amish would qualify as religious, the Court wrote, but a solitary seeker like the 19th Century Transcendentalist, Henry David Thoreau, would not. At the time, the question was mostly peripheral; hardly any Americans claimed to have their own, personal religions that would make it difficult for them to comply with civil law. In the intervening decades, though, American religion has changed. One-fifth of us—roughly 66 million people—now claim, like Thoreau, to follow our own, idiosyncratic spiritual paths. The New Thoreaus already have begun to appear in the cases, including recent vaccine mandate challenges, and courts will increasingly face the question whether purely idiosyncratic beliefs and practices qualify as religious for legal purposes. In this essay, I argue that Yoder’s insight was basically correct: the existence of a religious community is a crucial factor in the definition of religion. Religion cannot mean an exclusively communal phenomenon; a categorical rule would slight a long American tradition of respecting individual religious conscience and create difficult line-drawing problems. Nonetheless, the farther one gets from a religious community, the more idiosyncratic one’s spiritual path, the less plausible it is to claim that one’s beliefs and practices are religious, for legal purposes.
Keyphrase Extraction Using Enhanced Word and Document Embedding

We live in a world where information is available, in all areas of human activity, increasingly in digital text documents. It is necessary to explore the knowledge implied in these documents considering its fast-growing availability. The use of keywords provides for a more effective search for a document of interest, as keywords highlight a document's primary concept and, therefore, allow the researcher's interest to be readily aligned with that text. In this article, an unsupervised keyword extraction approach is proposed. The proposed approach retrofitted the concept of n-grams with state-of-the-art words and document embeddings. The approach simultaneously proposed a new method to compose document vectors using important word vectors and their idf-scores. Here we use higher-order word n-grams to improve various unigram embeddings and introduce a novel task to produce document embedding for document representation. The performance of the proposed embeddings is evaluated using four different datasets. The combination of higher-order word n-grams retrofitted Glove, and document embedding is the best embedding to be used for extracting key phrases. The bi-gram retrofitted embedding improves the results significantly over the baseline approaches.
A Dataset for Temporal Semantic Segmentation Dedicated to Smart Mobility of Wheelchairs on Sidewalks

In smart mobility, the semantic segmentation of images is an important task for a good understanding of the environment. In recent years, many studies have been made on this subject, in the field of Autonomous Vehicles on roads. Some image datasets are available for learning semantic segmentation models, leading to very good performance. However, for other types of autonomous mobile systems like Electric Wheelchairs (EW) on sidewalks, there is no specific dataset. Our contribution presented in this article is twofold: (1) the proposal of a new dataset of short sequences of exterior images of street scenes taken from viewpoints located on sidewalks, in a 3D virtual environment (CARLA); (2) a convolutional neural network (CNN) adapted for temporal processing and including additional techniques to improve its accuracy. Our dataset includes a smaller subset, made of image pairs taken from the same places in the maps of the virtual environment, but from different viewpoints: one located on the road and the other located on the sidewalk. This additional set is aimed at showing the importance of the viewpoint in the result of semantic segmentation.
Modal Shape Visualization Employing FP + 2D-DIC and Phased-Based Motion Magnification

Recently, the combination of Fringe Projection (FP) and 2D Digital Image Correlation (2D-DIC) has become a low-cost alternative for measuring deformations even in dynamic events such as vibration testing. FP and DIC are displacement measurement techniques, so high frequency vibration tests associated with low levels of displacement suppose a challenge. By means of Phase-Based Motion Magnification algorithm (PBMM), the periodic displacement observed in an image sequence can be magnified. This makes it possible to measure clear displacement maps by FP + 2D-DIC even when subtle displacement occurs. This methodology allows a better interpretation of the vibration behavior of mechanical components. In this work, the behavior of a beam excited at its natural frequencies has been studied, showing the potential of PBMM and FP + 2D-DIC
Hardware Efficient Hybrid Pseudo-Random Bit Generator Using Coupled-LCG and Multistage LFSR with Clock Gating Network

A new method for the generation of pseudo-random bits, based on a coupled-linear congruential generator (CLCG) and two multistage variable seeds linear feedback shift registers (LFSRs) is presented. The proposed algorithm dynamically changes the value of the seeds of each linear congruential generator (LCG) by utilizing the multistage variable seeds LFSR. The proposed approach exhibits several advantages over the pseudo-random bit generator (PRBG) methods presented in the literature. It provides low hardware complexity and high-security strength while maintaining the minimum critical path delay. Moreover, this design generates the maximum length of pseudo-random bit sequence with uniform clock latency. Furthermore, to improve the critical path delay, one more architecture of PRBG is proposed in this work. It is based on the combination of coupled modified-LCG with two variable seeds multistage LFSRs. The modified LCG block is designed by the two-operand modulo adder and XOR gate, rather than the three-operands modulo adder and shifting operation, while it maintains the same security strength. The clock gating network (CGN) is also used in this work to minimize the dynamic power dissipation of the overall PRBG architecture. The proposed architectures are implemented using Verilog HDL and further prototyped on commercially available field-programmable gate array (FPGA) devices Virtex-5 and Virtex-7. The realization of the proposed architecture in this FPGA device accomplishes an improved speed of PRBG, which consumes low power with high randomness compared to existing techniques. The generated binary sequence from the proposed algorithms has been verified for randomness tests using NIST statistical test suites.
Analytical Engineering for Data Stream

The analytical capacity of massive data has become increasingly necessary, given the high volume of data that has been generated daily by different sources.The data sources are varied and can generate a huge amount of data, which can be processed in batch or stream settings.The stream setting corresponds to the treatment of a continuous sequence of data that arrives in real-time flow and needs to be processed in real-time.The models, tools, methods and algorithms for generating intelligence from data stream culminate in the approaches of Data Stream Mining and Data Stream Learning.The activities of such approaches can be organized and structured according to Engineering principles, thus allowing the principles of Analytical Engineering, or more specifically, Analytical Engineering for Data Stream (AEDS).Thus, this article presents the AEDS conceptual framework composed of four pillars (Data, Model, Tool, People) and three processes (Acquisition, Retention, Review).The definition of these pillars and processes is carried out based on the main components of data stream setting, corresponding to four pillars, and also on the necessity to operationalize the activities of an Analytical Organization (AO) in the use of AEDS four pillars, which determines the three proposed processes.The AEDS framework favors the projects carried out in an AO, that is, its Analytical Projects (AP), to favor the delivery of results, or Analytical Deliverables (AD), carried out by the Analytical Teams (AT) in order to provide intelligence from stream data.
A well-known representation of monoids and its application to the function ‘vector reverse’

Abstract Vectors—or length-indexed lists—are classic example of a dependent type. Yet, most tutorials stay clear of any function on vectors whose definition requires non-trivial equalities between natural numbers to type check. This pearl shows how to write functions, such as vector reverse, that rely on monoidal equalities to be type correct without having to write any additional proofs. These techniques can be applied to many other functions over types indexed by a monoid, written using an accumulating parameter, and even be used to decide arbitrary equalities over monoids ‘for free.’
VAPER: A deep learning model for explainable probabilistic regression

A probabilistic regression model provides decision-makers with the regression output along with its quantitative uncertainty for given input variables. Even though this uncertainty may help to avoid serious consequences such as misdiagnosis or blackout due to overconfidence in the output, it only provides a measure of the uncertainty of the output, and has a limitation in that it cannot explain the reasons for the output and its uncertainty. If they can be presented along with their reasons, more suitable alternatives to the output can be found. However, despite the development of artificial intelligence methods to explain machine learning models and their outputs, few probabilistic regression models with this functionality have been proposed. Therefore, in this paper, we propose a variational autoencoder-based model for explainable probabilistic regression, called VAPER. VAPER provides a parametric probability distribution of an output variable over input variables and interprets it using layer-wise relevance propagation to investigate the effect of each input variable. To evaluate the effectiveness of the proposed model, we performed extensive experiments using several datasets. The experimental results demonstrated that VAPER has competitive regression performance compared to existing models, even with effective explainability.
Value evaluation of cultural tourism tourists’ psychological expectation based on machine learning data mining

The era of smart tourism has arrived. In the context of big data information, based on the thinking of the entire tourism activity, it is worth thinking about the role of tourism information in tourism activities. This paper proposes a method for evaluating the psychological expectations of tourist destinations by applying the quality function configuration. According to the needs of tourists, the relevant product characteristics of the tourist destination are selected, an evaluation quality house is established, and various relationships within the quality house are weighed, and established a mathematical model for the evaluation of tourists' psychological expectations in tourist destinations. Bringing the methods of machine learning (ML) and data mining (DM) into the research of tourists' psychological expectation value evaluation, ML is one of the main methods to solve the problem of DM. ML is the process of using the system itself to improve itself, therefore, ML is widely used in data mining. The research combines psychology and tourism research, through empirical research, to establish a structural equation model. It analyzes the influence of tourism information on tourists' behavioral decisions, increases the media's variable expectations of tourism, and uses tourist satisfaction and behavior as dependent variables. The results showed that the effect of tourism information on tourists is significantly greater than the expected effect (p = 0.510, P is significant at 0.001 level) than the effect of tourist satisfaction (p = 0.290, P is significant at 0.05 level). Therefore, in order to create good expectations for tourists, the general image of a tourist destination must match the actual local conditions. Using the support vector machine algorithm with the introduction of optimization mechanism to train the feature set of the user data, and then predict the links in Sina Weibo, and obtain higher prediction accuracy and prediction speed. The psychological expectation evaluation model of tourists in tourist destinations can effectively calculate the perceived value of psychological expectation evaluation of tourists in tourist destinations, and help tourists choose reasonable and satisfactory travel plans.
Tracing the minor gesture in painting: a shift from reflection to diffraction

It is commonly assumed that we come to know the world best by observing it and that innovation is a human centred phenomenon. However, the objective of this review is to synthesise literature that emphasises the under examined, yet crucial, role that materials and tools exert in generating embodied perception; a diffractive pattern experienced as a passage of intensity sensed in the body of the painter when interacting with the material aspects of painting. Drawing on new materialist philosophies, recent creative practice research and the practices of three contemporary painters: Bracha Ettinger, Jude Rae and Paul Ching-Bor, this review examines how this complex phenomenon, known as the ‘minor gesture’, occurs in the painting process. It concludes that common painting strategies activate this phenomenon and demonstrates that innovation has its origin, not only in the intentional acts of the painter, but also in embodiment during the painting process.
Proximization and dialogue in Internet news texts and comments

Abstract The paper presents the results of an empirical study on proximization approached as a dialogue between journalists and readers of Internet media texts and online comments. In this way the paper shows a practical application of the research programme, whose tenets I presented elsewhere ( Kowalski 2018 ), in relation to Polish and Romanian reciprocal media coverage on the winter 2016/2017 protests in the two countries, and follow-up online comments. The analysis shows that participants used proximization for different communicative purposes (descriptive or evaluative), for which they employed different reportoires of proximization strategies (categorization, domestication, construing hybrid IDC/ODC worlds, historical analogies, and references to cultural stereotypes). As a result, there was no consensus on a shared Discourse Space, and alternative Discourse Spaces were negotiated in the online dialogue.
Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model

Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy.