Reviewerly/exHarmony huggingface dataset:
Determining UAV Flight Trajectory for Target Recognition Using EO/IR and SAR

The paper presents the concept of planning the optimal trajectory of fixed-wing unmanned aerial vehicle (UAV) of a short-range tactical class, whose task is to recognize a set of ground objects as a part of a reconnaissance mission. Tasks carried out by such systems are mainly associated with an aerial reconnaissance using Electro-Optical/Infrared (EO/IR) systems and Synthetic Aperture Radars (SARs) to support military operations. Execution of a professional reconnaissance of the indicated objects requires determining the UAV flight trajectory in the close neighborhood of the target, in order to collect as much interesting information as possible. The paper describes the algorithm for determining UAV flight trajectories, which is tasked with identifying the indicated objectives using the sensors specified in the order. The presence of UAV threatening objects is taken into account. The task of determining the UAV flight trajectory for recognition of the target is a component of the planning process of the tactical class UAV mission, which is also presented in the article. The problem of determining the optimal UAV trajectory has been decomposed into several subproblems: determining the reconnaissance flight method in the vicinity of the currently recognized target depending on the sensor used and the required parameters of the recognition product (photo, film, or SAR scan), determining the initial possible flight trajectory that takes into account potential UAV threats, and planning detailed flight trajectory considering the parameters of the air platform based on the maneuver planning algorithm designed for tactical class platforms. UAV route planning algorithms with time constraints imposed on the implementation of individual tasks were used to solve the task of determining UAV flight trajectories. The problem was formulated in the form of a Mixed Integer Linear Problem (MILP) model. For determining the flight path in the neighborhood of the target, the optimal control algorithm was also presented in the form of a MILP model. The determined trajectory is then corrected based on the construction algorithm for determining real UAV flight segments based on Dubin curves.
Asymmetrical performance of a laser-based reservoir computer with optoelectronic feedback

We numerically quantify the performance of a photonic reservoir computer based on a semiconductor laser subject to high-pass filtered optoelectronic feedback. We assess its memory capacity, computational ability, and performance in solving a multi-step prediction task. By analyzing the complex bifurcation landscape of the corresponding delay-differential equation model, we observe that optimal performance occurs at the edge of instability, at the onset of periodic regimes, and unveil a parity asymmetry in the performance with a slight advantage for positive over negative feedback.
Anomaly detection and event mining in cold forming manufacturing processes

Abstract Predictive maintenance is one of the main goals within the Industry 4.0 trend. Advances in data-driven techniques offer new opportunities in terms of cost reduction, improved quality control, and increased work safety. This work brings data-driven techniques for two predictive maintenance tasks: anomaly detection and event prediction, applied in the real-world use case of a cold forming manufacturing line for consumer lifestyle products by using acoustic emissions sensors in proximity of the dies of the press module. The proposed models are robust and able to cope with problems such as noise, missing values, and irregular sampling. The detected anomalies are investigated by experts and confirmed to correspond to deviations in the normal operation of the machine. Moreover, we are able to find patterns which are related to the events of interest.
Quantum measurement incompatibility in subspaces

We consider the question of characterizing the incompatibility of sets of high-dimensional quantum measurements. We introduce the concept of measurement incompatibility in subspaces. That is, starting from a set of measurements that is incompatible, one considers the set of measurements obtained by projection onto any strict subspace of fixed dimension. We identify three possible forms of incompatibility in subspaces: (i) incompressible incompatibility---measurements that become compatible in every subspace, (ii) fully compressible incompatibility---measurements that remain incompatible in every subspace, and (iii) partly compressible incompatibility---measurements that are compatible in some subspace and incompatible in another. For each class, we discuss explicit examples. Finally, we present some applications of these ideas. First, we show that joint measurability and coexistence are two inequivalent notions of incompatibility in the simplest case of qubit systems. Second, we highlight the implications of our results for tests of quantum steering.
Disentangled Dynamic Graph Deep Generation

Deep generative models for graphs have exhibited promising performance in ever-increasing domains such as design of molecules (i.e, graph of atoms) and structure prediction of proteins (i.e., graph of amino acids). Existing work typically focuses on static rather than dynamic graphs, which are actually very important in the applications such as protein folding, molecule reactions, and human mobility. Extending existing deep generative models from static to dynamic graphs is a challenging task, which requires to handle the factorization of static and dynamic characteristics as well as mutual interactions among node and edge patterns. Here, this paper proposes a novel framework of factorized deep generative models to achieve interpretable dynamic graph generation. Various generative models are proposed to characterize conditional independence among node, edge, static, and dynamic factors. Then, variational optimization strategies as well as dynamic graph decoders are proposed based on newly designed factorized variational autoencoders and recurrent graph deconvolutions. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed models.
Authentic Drug Usage and Tracking with Blockchain Using Mobile Apps

&lt;span&gt;Tracking drugs became more difficult using the centralized architecture. Decentralized architecture using blockchain technology overcomes the difficulties faced by the centralized network like availability and recovery. Avoiding duplicate or fake drugs created by fake manufacturers is a big challenge in the centralized network. Authentic stock is managed and the supply chain is tracked efficiently using this blockchain technology. This is addressed by using the smart contract which helps to track the movement of drugs from manufacturer to supplier, supplier to the reseller, reseller to pharmacies and finally pharmacies to patient. By default, duplicate drugs or fake drugs are completely avoided by using the blockchain technology. Patients buy drugs without any prescription and it creates a lot of problems in real life. So, Patients cannot buy drugs without authenticated doctor’s prescriptions with the help of a QR Code scanner attached with the prescription which will be implemented using a mobile application and cannot buy excess drugs which might lose someone’s life. With the help of inventory management, the maximum limit of drugs to avoid overdose and pharmacies cannot sell those overdose drugs. Consulting a doctor before buying a drug for even a simple illness is important and it is tracked by using prescriptions provided by authentic doctors. In this project, these challenges are addressed using the smart contract which is written in solidity language and runs on a public ethereum network.&lt;/span&gt;
10 Clock-Periods Pipelined Implementation of AES-128 Encryption-Decryption Algorithm up to 28 Gbit/s Real Throughput by Xilinx Zynq UltraScale+ MPSoC ZCU102 Platform

The security of communication and computer systems is an increasingly important issue, nowadays pervading all areas of human activity (e.g., credit cards, website encryption, medical data, etc.). Furthermore, the development of high-speed and light-weight implementations of the encryption algorithms is fundamental to improve and widespread their application in low-cost, low-power and portable systems. In this scientific article, a high-speed implementation of the AES-128 algorithm is reported, developed for a short-range and high-frequency communication system, called Wireless Connector; a Xilinx ZCU102 Field Programmable Gate Array (FPGA) platform represents the core of this communication system since manages all the base-band operations, including the encryption/decryption of the data packets. Specifically, a pipelined implementation of the Advanced Encryption Standard (AES) algorithm has been developed, allowing simultaneous processing of distinct rounds on multiple successive plaintext packets for each clock period and thus obtaining higher data throughput. The proposed encryption system supports 220 MHz maximum operating frequency, ensuring encryption and decryption times both equal to only 10 clock periods. Thanks to the pipelined approach and optimized solutions for the Substitute Bytes operation, the proposed implementation can process and provide the encrypted packets each clock period, thus obtaining a maximum data throughput higher than 28 Gbit/s. Also, the simulation results demonstrate that the proposed architecture is very efficient in using hardware resources, requiring only 1631 Configurable Logic Blocks (CLBs) for the encryption block and 3464 CLBs for the decryption one.
Comparison of the accuracy of classification algorithms on three data-sets in data mining: Example of 20 classes

Data mining, which has different uses such as text mining and web mining, is especially used for clustering and classification purposes. In this study, this method was used for both classification and text mining. The aim of the study was the assessment of the performances of the data mining algorithms on the three datasets. A total of 6631 master's and doctoral dissertations written in the field of industrial engineering were downloaded from the Higher Education Council database. With the help of summary, subject titles and keywords of these dissertations, it was tried to be guessed which sub-field of industrial engineering it belongs to using WEKA program. As a result, it was observed that the data set containing the keywords obtained by weighting the expert opinion was more successful than the other two data sets. And the three most successful classification algorithms were found to be kNN, SMO, and J48, respectively.&#x0D; Keywords: Classification Algorithms, Data Mining, Multiple Classes, Dataset.
A Novel Framework Using Neutrosophy for Integrated Speech and Text Sentiment Analysis

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a plain text file. The neutrosophic set is symmetric in nature, and similarly refined neutrosophic set that has the refined indeterminacies I1 and I2 in the middle between the extremes Truth T and False F. Neutrosophy which deals with the concept of indeterminacy is another not so explored topic in NLP. Though neutrosophy has been used in sentiment analysis of textual data, it has not been used in speech sentiment analysis. We have proposed a novel framework that performs sentiment analysis on audio files by calculating their Single-Valued Neutrosophic Sets (SVNS) and clustering them into positive-neutral-negative and combines these results with those obtained by performing sentiment analysis on the text files of those audio.
“It’s Unwieldy and It Takes a Lot of Time” — Challenges and Opportunities for Creating Agents in Commercial Games

Game agents such as opponents, non-player characters, and teammates are central to player experiences in many modern games. As the landscape of AI techniques used in the games industry evolves to adopt machine learning (ML) more widely, it is vital that the research community learn from the best practices cultivated within the industry over decades creating agents. However, although commercial game agent creation pipelines are more mature than those based on ML, opportunities for improvement still abound. As a foundation for shared progress identifying research opportunities between researchers and practitioners, we interviewed seventeen game agent creators from AAA studios, indie studios, and industrial research labs about the challenges they experienced with their professional workflows. Our study revealed several open challenges ranging from design to implementation and evaluation. We compare with literature from the research community that address the challenges identified and conclude by highlighting promising directions for future research supporting agent creation in the games industry.
COUGH: A Challenge Dataset and Models for COVID-19 FAQ Retrieval

We present a large, challenging dataset, COUGH, for COVID-19 FAQ retrieval.Similar to a standard FAQ dataset, COUGH consists of three parts: FAQ Bank, Query Bank and Relevance Set.The FAQ Bank contains ∼16K FAQ items scraped from 55 credible websites (e.g., CDC and WHO).For evaluation, we introduce Query Bank and Relevance Set, where the former contains 1,236 human-paraphrased queries while the latter contains ∼32 humanannotated FAQ items for each query.We analyze COUGH by testing different FAQ retrieval models built on top of BM25 and BERT, among which the best model achieves 48.8 under P@5, indicating a great challenge presented by COUGH and encouraging future research for further improvement.Our COUGH dataset is available at https://github.com/sunlab-osu/covid-faq.
Intent Detection Problem Solving via Automatic DNN Hyperparameter Optimization

Accurate intent detection-based chatbots are usually trained on larger datasets that are not available for some languages. Seeking the most accurate models, three English benchmark datasets that were human-translated into four morphologically complex languages (i.e., Estonian, Latvian, Lithuanian, Russian) were used. Two types of word embeddings (fastText and BERT), three types of deep neural network (DNN) classifiers (convolutional neural network (CNN); long short-term memory method (LSTM), and bidirectional LSTM (BiLSTM)), different DNN architectures (shallower and deeper), and various DNN hyperparameter values were investigated. DNN architecture and hyperparameter values were optimized automatically using the Bayesian method and random search. On three datasets of 2/5/8 intents for English, Estonian, Latvian, Lithuanian, and Russian languages, accuracies of 0.991/0.890/0.712, 0.972/0.890/0.644, 1.000/0.890/0.644, 0.981/0.872/0.712, and 0.972/0.881/0.661 were achieved, respectively. The BERT multilingual vectorization with the CNN classifier was proven to be a good choice for all datasets for all languages. Moreover, in the majority of models, the same set of optimal hyperparameter values was determined. The results obtained in this research were also compared with the previously reported values (where hyperparameter values of DNN models were selected by an expert). This comparison revealed that automatically optimized models are competitive or even more accurate when created with larger training datasets.
Local Linear Regression Estimator on the Boundary Correction in Nonparametric Regression Estimation

The precision and accuracy of any estimation can inform one whether to use or not to use the estimated values.It is the crux of the matter to many if not all statisticians.For this to be realized biases of the estimates are normally checked and eliminated or at least minimized.Even with this in mind getting a model that fits the data well can be a challenge.There are many situations where parametric estimation is disadvantageous because of the possible misspecification of the model.Under such circumstance, many researchers normally allow the data to suggest a model for itself in the technique that has become so popular in recent years called the nonparametric regression estimation.In this technique the use of kernel estimators is common.This paper explores the famous Nadaraya-Watson estimator and local linear regression estimator on the boundary bias.A global measure of error criterion-asymptotic mean integrated square error (AMISE) has been computed from simulated data at the empirical stage to assess the performance of the two estimators in regression estimation.This study shows that local linear regression estimator has a sterling performance over the standard Nadaraya-Watson estimator.
Comparative Study of KNN, SVM and Decision Tree Algorithm for Student’s Performance Prediction

Students who are not-active will affect the number of students who graduate on time. Prevention of not-active students can be done by predicting student performance. The study was conducted by comparing the KNN, SVM, and Decision Tree algorithms to obtain the best predictive model. The model making process was carried out by the following steps: data collecting, pre-processing, model building, comparison of models, and evaluation. The results show that the SVM algorithm has the best accuracy in predicting with a precision value of 95%. The Decision Tree algorithm has a prediction accuracy of 93% and the KNN algorithm has a prediction accuracy value of 92%.
Development of an Al-Based Web Diagnostic System for Phenotyping Psychiatric Disorders

Background: Artificial Intelligence (AI) based medical diagnostic applications are on the rise. Our recent study has suggested an explainable deep neural network (EDNN) framework for identifying key structural deficits related to the pathology of schizophrenia. Here, we presented an AI-based web diagnostic system for schizophrenia under the EDNN framework with three-dimensional (3D) visualisation of subjects' neuroimaging dataset. Methods: This AI-based web diagnostic system consisted of a web server and a neuroimaging diagnostic database. The web server deployed the EDNN algorithm under the Node.js environment. Feature selection and network model building were performed on the dataset obtained from two hundred schizophrenic patients and healthy controls in the Taiwan Aging and Mental Illness (TAMI) cohort. We included an independent cohort with eighty-eight schizophrenic patients and forty-four healthy controls recruited at Tri-Service General Hospital Beitou Branch for validation purpose. Results: Our AI-based web diagnostic system achieved 84.00% accuracy (89.47% sensitivity, 80.62% specificity) for gray matter (GM) and 90.22% accuracy (89.21% sensitivity, 91.23% specificity) for white matter (WM) on the TAMI cohort. For the Beitou cohort as an unseen test set, the model achieved 77.27% and 70.45% accuracy for GM and WM. Furhtermore, it achieved 85.50% and 88.20% accuracy after model-retraining to mitigate the effects of drift on the predictive capability. Moreover, our system visualised the identified voxels in brain atrophy in a 3D manner with patients’ structural image, optimising the evaluation process of the diagnostic results. Discussion: Together, our approach under the EDNN framework demonstrated the potential future direction of making a schizophrenia diagnosis based on structural brain imaging data. Our deep learning model is explainable, arguing for the accuracy of the key information related to the pathology of schizophrenia when using the AI-based web assessment platform. The rationale of this approach is in accordance with the Research Domain Criteria suggested by the National Institute of Mental Health.
Cascaded Dilated Deep Residual Network for Volumetric Liver Segmentation From CT Image

Volumetric liver segmentation is a prerequisite for liver transplantation and radiation therapy planning. In this paper, dilated deep residual network (DDRN) has been proposed for automatic segmentation of liver from CT images. The combination of three parallel DDRN is cascaded with fourth DDRN in order to get final result. The volumetric CT data of 40 subjects belongs to “Combined Healthy Abdominal Organ Segmentation” (CHAOS) challenge 2019 is utilized to evaluate the proposed method. Input image converted into three images using windowing ranges and fed to three DDRN. The output of three DDRN along with original image fed to the fourth DDRN as an input. The output of cascaded network is compared with the three parallel DDRN individually. Obtained results were quantitatively evaluated with various evaluation parameters. The results were submitted to online evaluation system, and achieved average dice coefficient is 0.93±0.02; average symmetric surface distance (ASSD) is 4.89±0.91. In conclusion, obtained results are prominent and consistent.
Implementation of HOG Feature Extraction with Tuned Parameters for Human Face Detection

Extracting and tracking face in image sequences is a required first step in many applications such as face recognition facial expression classification and face tracking, it is a challenging problem in computer vision field because of many factors that effects on the image, some of these factors are luminosity, different face colors, background patterns, face orientation and variability in size, shape, and expression.The objective of this paper is to Experiment wide range of parameters for HOG face detector and setting up the most suitable kernel for Support Vector Machine (SVM) and then, comparing this method with some well-known methods for face detection and identifying the most reliable one.The aim of this study is not providing the best face detector method rather than a try to find out the performance of HOG feature for detecting a face, experimenting different kernels and eventually finding the tuned parameters for HOG descriptors for detecting a face, in this study based on experimental results as shown in Table IV.The HOG + SVM scores the highest value of precision, accuracy, and sensitivity.As 0.8824, 0.9986 and 0.75 respectively compared to Viola-Jones method which scores 0.6512, 0.9973 and 0.7 finally skin color method which scores 0.3968, 0.9947 and 0.625.
MSTD: Moroccan Sentiment Twitter Dataset

With the proliferation of social media and Internet accessibility, a massive amount of data has been produced.In most cases, the textual data available through the web comes mainly from people expressing their views in informal words.The Arabic language is one of the hardest Semitic languages to deal with because of its complex morphology.In this paper, a new contribution to the Arabic resources is presented as a large Moroccan dataset retrieved from Twitter and carefully annotated by native speakers.For the best of our knowledge, this dataset is the largest Moroccan dataset for sentiment analysis.It is distinguished by its size, its quality given by the commitment of annotators, and its accessibility for the research community.Furthermore, the MSTD (Moroccan Sentiment Twitter Dataset) is benchmarked through experiments carried out for 4-way classification as well as polarity classification (positive, negative).Various machine-learning algorithms are combined to feature extraction techniques to reach optimal settings.This work also presents the effect of stemming and lemmatization on the improvement of the obtained accuracies.