Reviewerly/exHarmony huggingface dataset:
An Improved Predictor-Corrector Guidance Algorithm for Reentry Glide Vehicle Based on Intelligent Flight Range Prediction and Adaptive Crossrange Corridor

For traditional predictor-corrector guidance algorithm for reentry glide vehicle, it cost a lot of time to obtain predicted flight range with a slow speed to iterate. In this paper, according to residual network (ResNet)’s block and dynamic model of vehicle, through analyzing the characteristics of predicted flight range with constraints, the flight range prediction block and flight range prediction neural network are designed, which can obtain the predicted range accurately and quickly; then aiming at the separation between guidance logic and no-fly zone avoidance logic, which may lead to guidance failure and increasing of the sign variation number of the bank angle, the no-fly zone crossrange and the no-fly zone mapping crossrange are proposed in this paper. According the repulsion force of artificial potential field, an adaptive crossrange corridor combining guidance logic and no-fly zone avoidance logic is proposed, and the convergence of the corridor is analyzed theoretically. Through simulation, the block number of flight range prediction network is determined firstly. By this method, the efficiency of lateral guidance can be improved. Then, through the simulation with the different no-fly zones under different disturbed conditions, the stability and validity of the guidance method are verified. Finally, compared with other predictor-corrector algorithms, the proposed method can realize guidance with less sign variation number of bank angle and better avoidance for no-fly zones.
Eagle Vision-Based Coordinate Landing Control Framework of Unmanned Aerial Vehicles on an Unmanned Surface Vehicle

The coordinate landing control of the unmanned aerial vehicles (UAVs) is a key technology to expand the cooperation between the UAVs and the unmanned surface vehicle (USV). This paper provides an eagle vision-based coordinate landing control framework for the UAV swarm landing on a USV. Back-stepping controller is designed to guarantee that the tracking errors in both of the approaching stage and the landing stage converge to zero. Eagle vision is applied in the visual navigation to improve the landing accuracy. The experiment verified the feasibility of the proposed method.
Combining sentiment analysis classifiers to explore multilingual news articles covering London 2012 and Rio 2016 Olympics

Abstract This study aims to present an approach for the challenges of working with Sentiment Analysis (SA) applied to news articles in a multilingual corpus. It looks at the use and combination of multiple algorithms to explore news articles published in English and Portuguese. It presents a methodology that starts by evaluating and combining four SA algorithms (SenticNet, SentiStrength, Vader and BERT, being BERT trained in two datasets) to improve the quality of outputs. A thorough review of the algorithms’ limitations is conducted using SHAP, an explainable AI tool, resulting in a list of issues that researchers must consider before using SA to interpret texts. We propose a combination of the three best classifiers (Vader, Amazon BERT and Sent140 BERT) to identify contradictory results, improving the quality of the positive, neutral and negative labels assigned to the texts. Challenges with translation are addressed, indicating possible solutions for non-English corpora. As a case study, the method is applied to the study of the media coverage of London 2012 and Rio 2016 Olympic legacies. The combination of different classifiers has proved to be efficient, revealing the unbalance between the media coverage of London 2012, much more positive, and Rio 2016, more negative.
Generalization of Tsallis Entropy-Based Fuzzy <i>c</i>-Means Clustering and its Behavior at the Infinity Point

This study presents a generalized Tsallis entropy-based fuzzy c -means (GTFCM) clustering algorithm. Furthermore, the results of this study show that the behavior of GTFCM, at an infinity point of the fuzzy classification function, is similar to that of some conventional clustering algorithms. This result implies that such behavior is determined by a certain part of the GTFCM objective function.
Mining fuzzy sub-prevalent co-location pattern with dominant feature

Prevalent co-location pattern mining aims to find a subset of spatial features whose instances are frequently located together in geo-space. Sub-prevalent co-location pattern mining attains co-location patterns with richer spatial relationships based on star instance model instead of clique instance model. Further, discovering dominant features in a sub-prevalent co-location pattern is important to reveal the interaction relationship between spatial features and improve the application of the pattern. However, the methods for mining sub-prevalent co-location pattern with dominant feature use a distance threshold to determine the binary neighbor relationship between spatial instances, and ignore the fuzziness of neighbor relationship which can give a rise to the fuzziness of dominance relationship between spatial features. Thus, this paper presents mining fuzzy sub-prevalent co-location pattern with dominant feature by exploring spatial instance distribution based on the fuzzy theory. Specifically, a novel pattern, fuzzy sub-prevalent co-location pattern with dominant feature, is proposed by defining the fuzzy neighbor relationship between spatial instances and the fuzzy dominant relationship between spatial features. Then, an efficient algorithm to mine the proposed patterns is designed by utilizing the anti-monotonicity of fuzzy star participation index and fuzzy star dominance index to prune unpromising patterns. The experimental results show that the proposed patterns are practical and the mining algorithm is efficient.
Characteristics, provenance, ore genesis and exploration of the Shimshal Valley placer gold deposits in the Karakoram Block, North Pakistan

The Karakoram Block in North Pakistan formed during the late Permian-Triassic on the southern margin of Asian Plate with a topography characterized by glaciated high mountains. Several rivers and streams draining from these mountain peaks carry detritus with precious and base metals. However, the primary source deposits and their ore genesis remain unknown due to the rugged glaciated topography and inaccessibility for prospecting and in situ sampling. Here, we present a multi-parametric analytical approach including major and trace element characteristics, thermoelectric analysis, EPMA elemental mapping, XRD analysis, and sulfur and lead isotopic studies on the detrital auriferous pyrite from Shimshal Valley placer gold deposits in the Karakoram Block of the north Pakistan to gain insights into the ore genesis, provenance and current erosion or denudation rate of the source primary deposits found in the hinterlands of the Shimshal Valley. The pyrite with varying forms such as {100} + {111} + {210} followed by {210} + {111} and {100} + {210}, which are highly enriched in gold (up to 1700 ppm) as revealed through EPMA analysis. The low to moderate temperature estimated for their formation (100 to 360 °C), pyrite geochemical characteristics, geochemical discrimination diagrams (Fe/S-As, Fe + S-As, As-Co-Ni) and elements contents (Au, Ag, Cu) of the placer gold grains, as well as the high (58.2 %) and low (41.8 %) thermoelectric coefficients of N-type and P-type, suggest that the auriferous pyrite belong to the epithermal type of magmatic-hydrothermal systems. The XnP value (average −48.7 %), γ value (average 62.8 %) in conjunction with the n(S)/n(Fe) values of 1.97–2.11 of pyrite indicate that the placer gold deposits are derived from the middle-upper part of the ore body. The high Mo/Ni (average 5.4) and Co/Ni (average 2.7) ratio combined with detrital zircon UPb geochronology, zircon fertility indicators such as Eu/Eu*, Ce/Nd, (Ce/Nd)/Y, Dy/Yb, (Eu/Eu*)/Y, texture and REE patterns correlate the ore mineralization with the Hunza Plutonic Unit emplaced on the catchment of the valley. The narrow variations in δ34S values, ranging from −5.3 to 3.8 ‰ with an average of −4.7 ‰, suggest that sulfur has a homogeneous magmatic source. The Pb isotopes values range 18.487 to 18.692, 15.713 to 15.745, and 38.964 to 39.416 for 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb, respectively. Our results suggest that metals for ore-forming fluid involved considerable contribution of crustal-derived materials.
<scp>ExplainFix</scp>: Explainable spatially fixed deep networks

Is there an initialization for deep networks that requires no learning? ExplainFix adopts two design principles: the "fixed filters" principle that all spatial filter weights of convolutional neural networks can be fixed at initialization and never learned, and the "nimbleness" principle that only few network parameters suffice. We contribute (a) visual model-based explanations, (b) speed and accuracy gains, and (c) novel tools for deep convolutional neural networks. ExplainFix gives key insights that spatially fixed networks should have a steered initialization, that spatial convolution layers tend to prioritize low frequencies, and that most network parameters are not necessary in spatially fixed models. ExplainFix models have up to 100x fewer spatial filter kernels than fully learned models and matching or improved accuracy. Our extensive empirical analysis confirms that ExplainFix guarantees nimbler models (train up to 17\% faster with channel pruning), matching or improved predictive performance (spanning 13 distinct baseline models, four architectures and two medical image datasets), improved robustness to larger learning rate, and robustness to varying model size. We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.
Benchmarking Gradient Estimation Mechanisms in Evolution Strategies for Solving Black-Box Optimization Functions and Reinforcement Learning Problems

In this paper, we investigate the gradient estimation mechanisms of three evolution strategies (ES) algorithms: the vanilla ES (VES), the Guided Evolutionary Strategies (GES), and the Self-Guided Evolution Strategies (SGES). The vanilla ES generates search directions (i.e., its population individuals) following an isotropic Gaussian distribution from the full parameter space, yielding unbiased estimations of the true gradient vectors but suffering from sample inefficiency in high-dimensional problems. GES and SGES aim to construct low-dimensional guiding subspaces, that potentially contain the true gradients, from which search directions for computing gradient estimates can be generated in a more efficient manner. We perform experiments with a variety of high-dimensional optimization problems, including multi-modal black-box functions and noisy reinforcement learning locomotion tasks. Experimental results help pinpoint the essential components of these algorithms and the important issues that need to be considered for their successful applications in solving challenging optimization problems. Source code is available at: https://github.com/ELO-Lab/BenchGEM-ES.
Social Network Users Profiling Using Machine Learning for Information Security Tasks

The need for bot detection is growing in proportion to the increase in the number of social network users. The robotization of processes has not escaped social networks, with the result that bots, designed to mimic human behavior, create a burden and, in some cases, threats to users, including manipulation and misinformation. Classical information security threats related to bot activity are DDoS, collection and distribution of user data, manipulation of billing systems, and misuse of services. Often bot technology is used for scoring bonus points or using other customer loyalty mechanisms to gain their own benefit, in violation of the service policy. The problem is that it is often hard to confirm the correspondence between a real person and a profile due to the large amount of disparate information about users' activity, as well as the use of modern technologies, including machine learning, to develop bots. This paper focuses on the problem of detecting bots in social networks using machine learning. We propose an automatic, retrainable method for detecting fake accounts on a social network. The study describes the result of developing user classification models based on the activity logs of social network users in the problem of automated user profiling, that is, determining whether a user account is genuine or a bot is hiding behind it. The aim of the work is to develop methods for detecting bots using machine learning and intelligent analysis. In our work to solve the problem we use gradient boosting with an accuracy of AUC = 0.9999.
Improved Genetic Optimized Feature Selection for Online Sequential Extreme Learning Machine

Extreme learning machine (ELM) is a rapid classifier, evolved for batch learning mode which is not suitable for sequential input. As retrieving of data from new inventory which is leads to time extended process. Therefore, online sequential ELM (OSELM) algorithm is progressed to handle the sequential input in which data is read 1 by 1 or chunk by chunk mode. The overall system generalization performance may devalue because of the amalgamation of random initialization of OS-ELM and the presence of redundant and irrelevant features. To resolve the said problem, this paper proposes a correspondence improved genetic optimized feature selection paradigm for sequential input (IG-OSELM) for radial basis or function by using clinical datasets. For performance comparison, the proposed paradigm experimented and evaluated for ELM, improved genetic optimized for ELM classifier (IG-ELM), OS-ELM, IG-OSELM. Experimental results are calculated and analyzed accordingly. The comparative results analysis illustrates that IG-ELM provides 10.94% improved accuracy with 43.25% features as compared to ELM.
Learning by teaching in immersive virtual reality – Absorption tendency increases learning outcomes

We investigated the learning outcome of teaching an agent via immersive virtual reality (IVR) in two experiments. In Experiment 1, we compared IVR to a less immersive desktop setting and a control condition (writing a summary). Learning outcomes of participants who had explained the topic to an agent via IVR were better. However, this was only the case for participants who scored high on absorption tendency. In Experiment 2, we investigated whether including social cues in the task instructions enhances learning in participants explaining a topic to an agent. Instruction manipulation affected learning as a function of absorption tendency: Low-absorption participants benefitted most from being instructed to imagine they were helping a student peer pass an upcoming test, while high-absorption participants benefitted more when they were to explain the text to a virtual agent. The findings highlight the crucial role of personality traits in learning by teaching in IVR.
Receptive Field Analysis for Optimizing Convolutional Neural Network Architectures Without Training

The number of layers in convolutional neural networks (CNN) is often overshot, when a convolutional neural network architecture is designed for an image-based task. These CNN-architectures are therefore unnecessarily costly to train and deploy. The increase in network-complexity also results in diminishing returns in terms of the predictive quality. The receptive field of a convolutional layer is strictly limiting the features it can process. We can consistently predict unproductive layers that will not contribute qualitatively to the test performance in a given CNN architecture, by analyzing the receptive field expansion over the network. Since the receptive field is a property of the architecture itself, this analysis does not require training the model. We refer to this analysis technique as Receptive Field Analysis (RFA). In this work, we demonstrate that RFA can be used to guide the optimization of CNN architectures by predicting the presence of unproductive layers. We show that RFA allows the deduction of design decisions and simple design strategies that reliably improve the parameter efficiency of the model on the given task. We further demonstrate that these RFA-guided strategies can reliably improve the predictive performance, computational efficiency or strike a balance between the two. Finally, we show that RFA can also be used to define an interval of feasible input resolutions for any modern architecture, in which the model will operate with high efficiency, while being able to extract any pattern from the image. This allows practitioners to pick efficient input resolutions when adapting models for novel tasks.
Particle size distribution inversion using the Weibull-distribution adaptive-parameters cuckoo search algorithm

A Weibull-distribution adaptive-parameters cuckoo search (WACS) algorithm is proposed, which can converge quickly and prevent falling into local optimal values, and thus improve the global search performance of a cuckoo search (CS) algorithm. In simulations, particle size inversions were performed using the proposed algorithm for unimodal and bimodal particle systems obeying Johnson's SB, Rosin-Rammler, and normal distribution, and the results were compared to the original CS algorithm, Weibull-distribution CS algorithm, and adaptive-parameters CS algorithm. Among them, the WACS algorithm has the best accuracy. The relative root mean squared error (RRMSE) was three to four orders of magnitude lower than the CS algorithm. The noise immunity of the algorithm was verified by comparing the particle size inversion error. Random noise [1%, 10%] was added to the scattered light energy of the target function, in 1% noise increments. The WACS algorithm prevailed, and the advantage became more obvious as the noise increased. A small-angle forward scattering experimental platform was built, and ferric tetroxide particles were selected as the measured particles. Experimental measurements were carried out on a unimodal particle system (50 µm) and bimodal particle system (50 and 100 µm), while the WACS algorithm was used on particle size distribution inversion. Compared to the CS algorithm, the RRMSE of the WACS algorithm was approximately 51% lower on unimodal and 66% lower on bimodal particle population inversions.
Embedding generation for text classification of Brazilian Portuguese user reviews: from bag-of-words to transformers

Text classification is a natural language processing (NLP) task relevant to many commercial applications, like e-commerce and customer service. Naturally, classifying such excerpts accurately often represents a challenge, due to intrinsic language aspects, like irony and nuance. To accomplish this task, one must provide a robust numerical representation for documents, a process known as embedding. Embedding represents a key NLP field nowadays, having faced a significant advance in the last decade, especially after the introduction of the word-to-vector concept and the popularization of Deep Learning models for solving NLP tasks, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Transformer-based Language Models (TLMs). Despite the impressive achievements in this field, the literature coverage regarding generating embeddings for Brazilian Portuguese texts is scarce, especially when considering commercial user reviews. Therefore, this work aims to provide a comprehensive experimental study of embedding approaches targeting a binary sentiment classification of user reviews in Brazilian Portuguese. This study includes from classical (Bag-of-Words) to state-of-the-art (Transformer-based) NLP models. The methods are evaluated with five open-source databases with pre-defined data partitions made available in an open digital repository to encourage reproducibility. The Fine-tuned TLMs achieved the best results for all cases, being followed by the Feature-based TLM, LSTM, and CNN, with alternate ranks, depending on the database under analysis.
Automatic refilling vending machine using Amazon DRS AWS

Presently, the existing replenishment device may be handiest when systematically reviewing the inventories. Whereas, there are ongoing vending machines that may not be able to provide or discover whether the substances are doing nicely to be replenished or no longer be replenished. This effect is an inefficient replenishment policy and there will be regular inventory-out among the products. This undertaking intends to provide the automatic replenishment of smart merchandising gadget with the intention to provide and support inventories to the administrator. Amazon sprint replenishment service (Amazon DRS) permits, while the vending device is about to run out of the stock/product, it to automatically locate orders on Amazon, administering which of the supplies may be due for replenishment. By executing this venture, inventory-out of the vending machines may be prevented and a green manner of the replenishment system can be carried out.
Spin-orbit maximally discordant mixed states

We introduce a proposal to prepare spin-obit maximally discordant mixed states by a linear optical circuit, with quantum bits (qubits) encoded in the polarization and transverse mode degrees of freedom of photons. In particular, we discuss how to prepare nonbalanced spin-orbit entangled states, applying this technique to obtain maximally discordant mixed states. We present a simulation of the optical circuit by using the Jones matrix formalism. We performed a study of entanglement, classical, and quantum correlations. The results show excellent agreement with the underlying theory and open an alternative experimental approach for addressing quantum correlations in optical setups.
Hi-LASSO: High-performance python and apache spark packages for feature selection with high-dimensional data

High-dimensional LASSO (Hi-LASSO) is a powerful feature selection tool for high-dimensional data. Our previous study showed that Hi-LASSO outperformed the other state-of-the-art LASSO methods. However, the substantial cost of bootstrapping and the lack of experiments for a parametric statistical test for feature selection have impeded to apply Hi-LASSO for practical applications. In this paper, the Python package and its Spark library are efficiently designed in a parallel manner for practice with real-world problems, as well as providing the capability of the parametric statistical tests for feature selection on high-dimensional data. We demonstrate Hi-LASSO’s outperformance with various intensive experiments in a practical manner. Hi-LASSO will be efficiently and easily performed by using the packages for feature selection. Hi-LASSO packages are publicly available at https://github.com/datax-lab/Hi-LASSO under the MIT license. The packages can be easily installed by Python PIP, and additional documentation is available at https://pypi.org/project/hi-lasso and https://pypi.org/project/Hi-LASSO-spark .
Sentimental Analysis and Classification of Restaurant Reviews

Restaurants these days like taking web-based orders. It is not just aiding in getting viable client’s feedback but additionally valuable for overseeing orders without any problems. We are moving toward a mechanized and advanced world. Having a huge presence on the web is essential for any restaurant to find true success and prosperity. Getting client’s feedback and examining them in a powerful way has an effect. Furthermore, it presents helpful data that the evaluations do not consider or neglect. Consolidated research is finished utilizing two different datasets of restaurant reviews in this paper. Machine learning calculations like Naïve Bayes and Logistic Regression are utilized for first grouping the surveys into legitimate perspectives and then performing opinion investigation on them. This study investigates restaurant reviews taken from Kaggle and yielding an exactness score of 66.66% for Naive Bayes and 92.22% for Logistic Regression.