Reviewerly/exHarmony huggingface dataset:
Class-Oriented Features Selection Technology in Medical Images Classification Problem on the Example of Distinguishing Between Tuberculosis Sensitive and Resistant Forms

This work proposes a class oriented technology for features ensembles formation based on preliminary sequential selection by interclass and intraclass dispersion criteria for final promising ensembles selection by the combinational criterion of correlation with classes of the most pairwise independent features. The speeding up of the complete ensembles selection at the final stage is realized by the self-organization procedure based on a genetic algorithm. Depending on the primary features set structure, it is possible to obtain a design of both a single sequential selection technology and parallel processing according to given channels, which are combined on the final stage. The Random Forest algorithm was used for classification with optimization of the voting function by the group method of data handling. The provided technology was used for differentiation of sensitive and resistant tuberculosis on patient CT images, which is one of the urgent needs of modern medicine. We used matrix elements of textural features as primary features: Grayscale Colors Matching Matrix, Grayscale Spacing Matrix, Grayscale Leap Length Matrix, Grayscale Tone Difference Matrix, Grayscale Dependency Matrix, Gray Level Histogram Matrix. For the regions of interest highlighted in the images, high quality of classification was obtained: 85&#x0025; accuracy in the examination sample. Data for the research were provided by the State Institution &#x201C;F.G. Yanovsky National Institute of Phthisiology and Pulmonology of the NAMS of Ukraine&#x201D;.
Detecting Audio Adversarial Examples with Logit Noising

Automatic speech recognition (ASR) systems are vulnerable to audio adversarial examples that attempt to deceive ASR systems by adding perturbations to benign speech signals. Although an adversarial example and the original benign wave are indistinguishable to humans, the former is transcribed as a malicious target sentence by ASR systems. Several methods have been proposed to generate audio adversarial examples and feed them directly into the ASR system (over-line). Furthermore, many researchers have demonstrated the feasibility of robust physical audio adversarial examples (over-air). To defend against the attacks, several studies have been proposed. However, deploying them in a real-world situation is difficult because of accuracy drop or time overhead.
An Improved GWO Algorithm Optimized RVFL Model for Oil Layer Prediction

In this study, a model based on the improved grey wolf optimizer (GWO) for optimizing RVFL is proposed to enable the problem of poor accuracy of Oil layer prediction due to the randomness of the parameters present in the random vector function link (RVFL) model to be addressed. Firstly, GWO is improved based on the advantages of chaos theory and the marine predator algorithm (MPA) to overcome the problem of low convergence accuracy in the optimization process of the GWO optimization algorithm. The improved GWO algorithm was then used to optimize the input weights and implicit layer biases of the RVFL network model so that the problem of inaccurate and unstable classification of RVFL due to the randomness of the parameters was avoided. MPA-GWO was used for comparison with algorithms of the same type under a function of 15 standard tests. From the results, it was concluded that it outperformed the algorithms of its type in terms of search accuracy and search speed. At the same time, the MPA-GWO-RVFL model was applied to the field of Oil layer prediction. From the comparison tests, it is concluded that the prediction accuracy of the MPA-GWO-RVFL model is on average 2.9%, 3.04%, 2.27%, 8.74%, 1.47% and 10.41% better than that of the MPA-RVFL, GWO-RVFL, PSO-RVFL, WOA-RVFL, GWFOA-RVFL and RVFL algorithms, respectively, and its practical applications are significant.
Cross-Device Augmented Reality Annotations Method for Asynchronous Collaboration in Unprepared Environments

Augmented Reality (AR) annotations are a powerful way of communication when collaborators cannot be present at the same time in a given environment. However, this situation presents several challenges, for example: how to record the AR annotations for later consumption, how to align virtual and real world in unprepared environments or how to offer the annotations to users with different AR devices. In this paper we present a cross-device AR annotation method that allows users to create and display annotations asynchronously in environments without the need for prior preparation (AR markers, point cloud capture, etc.). This is achieved through an easy user-assisted calibration process and a data model that allows any type of annotation to be stored on any device. The experimental study carried out with 40 participants has verified our two hypotheses: we are able to visualize AR annotations in indoor environments without prior preparation regardless of the device used and the overall usability of the system is satisfactory.
IMM-CKF for a Highly Maneuvering Target Using Converted Measurements

We consider the state estimation of a highly maneuvering aircraft using an air moving target indicator (AMTI) radar. The AMTI radar measures the range, azimuth, and radial velocity of the target. In our previous work, we used the interacting multiple model (IMM) filter with the extended Kalman filter (EKF), unscented Kalman filter (UKF), and cubature Kalman filter (CKF) using the AMTI measurements. In this paper, we use the converted measurements in an IMM-CKF and analyze the performances of these two types of algorithms. The converted measurements include the unbiased converted measurement (UCM) and modified unbiased converted measurement (MUCM) or conditional mean. The performances of the filters are analyzed using the root mean square position and velocity errors, root time-averaged mean square (RTAMS) error, average normalized estimation error squared (ANEES), mode probabilities, and computational cost. We also compute the posterior Cramér-Rao lower bound to evaluate these two types of algorithms.
FedDCS: Federated Learning Framework based on Dynamic Client Selection

Federated Learning, through which a server can coordinate a crowd of clients to accomplish a machine learning task, has been recognized as a promising paradigm for privacy preserving decentralized learning in recent years. Most federated learning researches are on the basis of IID data, and more and more researches focus on the Non-IID data problem. However, they have not considered the process of data acquisition. In fact, in real applications, the training data are typically collected by clients in real scenarios. In this paper, we propose a Federated Learning framework based on Dynamic Client Selection, called FedDCS, to deal with the real scene Non-IID data machine learning problem in federated learning. The objective of FedDCS is to utilize a parameter estimation algorithm to select the optimal clients to join the collaboration and finally acquire a better global machine learning model. Our extensive experiments on several real-world data sets demonstrate the superior performance of FedDCS.
Hallucinating uncertain motion and future for static image action recognition

Static image action recognition, aiming to recognize human action in a single image, is a challenging task due to the lack of motion and temporal information in static images. Therefore, some previous works have leveraged freely available unlabeled videos to assist image action recognition, which can be categorized into the following two research lines: predict motion or future information of static images using the predictor learned from unlabeled videos. In this paper, following the above two research lines, we propose a novel Multi-modal Motion feature Generator (MMG) and a novel Multi-modal Future feature Generator (MFG) to hallucinate multiple plausible motion features and future visual features for a static image, which could significantly facilitate the image action recognition task. Extensive experiments on two video datasets and two static action image datasets demonstrate the effectiveness of our methods. • We hallucinate multiple plausible motion features for a static image. • We hallucinate multiple plausible future features for a static image. • Hallucinated features can facilitate static image action recognition.
MT Evaluation in the Context of Language Complexity

The paper focuses on investigating the impact of artificial agent (machine translator) on human agent (posteditor) using a proposed methodology, which is based on language complexity measures, POS tags, frequent tagsets, association rules, and their summarization. We examine this impact from the point of view of language complexity in terms of word and sentence structure. By the proposed methodology, we analyzed 24 733 tags of English to Slovak translations of technical texts, corresponding to the output of two MT systems (Google Translate and the European Commission’s MT tool). We used both manual (adequacy and fluency) and semiautomatic (HTER metric) MT evaluation measures as the criteria for validity. We show that the proposed methodology is valid based on the evaluation of frequent tagsets and rules of MT outputs produced by Google Translate or of the European Commission’s MT tool, and both postedited MT (PEMT) outputs using baseline methods. Our results have also shown that PEMT output produced by Google Translate is characterized by more frequent tagsets such as verbs in the infinitive with modal verbs compared to its MT output, which is characterized by masculine, inanimate nouns in locative of singular. In the MT output, produced by the European Commission’s MT tool, the most frequent tagset was verbs in the infinitive compared to its postedited MT output, where verbs in imperative and the second person of plural occurred. These findings are also obtained from the use of the proposed methodology for MT evaluation. The contribution of the proposed methodology is an identification of systematic not random errors. Additionally, the study can also serve as information for optimizing the translation process using postediting.
A Systematic Comparative Study of Linear, Binary and Interpolation Search Algorithms

The searching denotes a process that aims to find a specific item in the data set. After performing the process of searching, the desired items are likely found or not. This kind of investigations was actually demonstrated by some researchers. For each search algorithm, the previous studies just focused to obtain the search time but did not obtain the number of passed-nodes. Furthermore, the results obtained with regards to the search time were not reported systematically. Hence, this study investigates how fast the desired items were found and how few passed-nodes were obtained when searching desired items by using three search algorithms. These algorithms were linear, binary, and interpolation algorithms. By using search algorithms, one hundred twenty desired items were searched in a collection of active-student data (3946 records in total). The search time needed to find a specific item and the number of passed nodes were recorded. The results showed that the searching process with linear search algorithm required more time and more passed-nodes than binary and interpolation search algorithms. Following this, searching time and the number of passed-nodes for desired items were significantly smaller for binary search than for the interpolation search algorithm. Taken together, the present results suggested that the binary search could be considered as a solid algorithm in searching a specific item in the data set utilized by the current study.
Time Scaling Detection and Estimation in Audio Recordings

The widespread diffusion of user friendly editing software for audio signals has made audio tampering extremely accessible to anyone. Therefore, it is increasingly necessary to develop forensic methodologies aiming at verifying if a given audio content has been digitally manipulated or not. Among the multiple available audio editing techniques, a very common one is time scaling, i.e., altering the temporal evolution of an audio signal without affecting any pitch component. For instance, this can be used to slow-down or speed-up speech recordings, thus enabling the creation of natural sounding fake speech compositions. In this work, we propose to blindly detect and estimate the time scaling applied to an audio signal. To expose time scaling, we leverage a Convolutional Neural Network that analyzes the Log-Mel Spectrogram and the phase of the Short Time Fourier Transform of the input audio signal. The proposed technique is tested on different audio datasets, considering various time scaling implementations and challenging cross test scenarios.
Color Image Quality Evaluation based on Visual Saliency and Gradient Information

In the field of image quality evaluation, visual saliency and gradient information are very effective features for quality evaluation models. Visual saliency is often used to study which areas of an image are most attractive to the human visual system. Moreover, the degradation of gradient information can reflect the degree of structure distortion of images. Considering these two points, we propose a simple but very effective quality evaluation metric for color images. After obtaining the local gradient similarity information, the similarity of visual saliency and color information are also calculated, and then we calculate the standard deviations of the three components to obtain the final quality score. The experimental results from five benchmark databases (LIVE, IVC, TID2008, TID2013 and CSIQ) show that our model performs better than other methods in the correlation with human visual quality judgment.
Masa: AI-Adaptive Mobile App for Sustainable Agriculture

Mobile applications have made significant positive contributions to our daily lives in recent years. They have greatly improved practices in critical sectors such as healthcare, education, and agriculture etc. The agricultural sector is under pressure to meet the ever-increasing food demand while needing to deal with lack of agricultural resources (such as water and soil) and climate change issues. There is also a need to figure out how to attract and involve young people in agriculture in order to replace aging farmers. These issues necessitate the development of new, sustainable agricultural solutions. This is the demand that Masa app is attempting to meet. This paper presents the design and implementation of an AI-adaptive mobile app for sustainable agriculture. The app is divided into two sections: the market section which creates a platform where buyers are able to connect with the farmers directly and the resource center section where the power of AI is used to give guidance to the farmers. It also has learning resources where new entrants into the Agric sector can have first-hand walk-through guides in the Agric field containing all the information they need to venture into farming any product of choice. This will help mitigate some of the challenges faced by people who are new or venturing into the agricultural sector thereby encouraging more people to enter agriculture hence its sustainability. Initial feedback on the application from researchers of human-computer interaction domain show the application's promise in closing the gap in farming knowledge among general people while highlighting some limitations and suggestion that can be considered for improving the application.
A* Algorithm Path Planning and Minimum Snap Trajectory Generation for Mobile Robot

This paper proposes a hybrid algorithm combining the improved A* algorithm, the minimum snap trajectory generation, and the timed elastic band, to solve the problem of mobile robot path planning in complex environments. To improve the efficiency of the path planning algorithm passing through multiple target points in a single run, and to improve the flexibility of path smoothing, the target cost function is used to prioritize all targets, and the improved A* algorithm is used to plan an optimal path. Meanwhile, the minimum snap trajectory generation is used to effectively shorten the path length and reduce the total turning angle. In addition, an online path planning method combining the timed elastic band and the global path planning information is proposed for mobile robots to avoid the local obstacles and meet the non-holonomic constraints of mobile robots. Finally, the simulation experiments are carried out to evaluate the effectiveness of the proposed method. The results show that it can achieve path planning more effectively.
Asymmetric Data Hiding for Compressed Images with High Payload and Reversibility

Hiding secret data in digital images is an attractive topic in the information security research area. Because the data-embedded stego image looks exactly the same as a regular image, transmitting secret data with stego images does not draw the attention of eavesdroppers, thus fulfilling the goal of information security. Many reversible data hiding (RDH) methods for absolute moment block truncation coding (AMBTC) compressed images have been proposed. These methods hide secret data in an AMBTC-compressed image to produce a stego image and transmit it to the recipient. Upon receiving the stego image, the recipient can extract the secret data and recover the AMBTC-compressed image. In this paper, we propose an RDH scheme for AMBTC-compressed images with an asymmetric embedding rule. Using the AMBTC-compressed version as the basis, the proposed embedding scheme always modifies a pixel value toward its original value with a step size (bitrate) proportional to the gap width. Therefore, the visual quality of the stego image is better than the referred AMBTC version. Additionally, as a result of the adaptive bitrate strategy, the data embedding capacity of the proposed scheme outperforms that of state-of-the-art methods. The security of the resulting stego images was also tested by RS-steganalysis. Experimental results show that the overall performance of the proposed scheme is satisfactory. We revised it, please confirm.
Towards a Taxonomy of Agile Methods: The Tree of Agile Elements

Agile methods require constant optimization of one&#x2019;s approach and leading to the adaptation of agile practices. These practices are also adapted when introducing them to companies and their software development teams due to organizational constraints. As a consequence of the widespread use of agile methods, we notice a high variety of their elements: Practices, roles, and artifacts. This multitude of agile practices, artifacts, and roles results in an unsystematic mixture. It leads to several questions: When is a practice a practice, and when is it a method or technique? This paper presents the tree of agile elements, a taxonomy of agile methods, based on the literature and guidelines of widely used agile methods. We describe a taxonomy of agile methods using terms and concepts of software engineering, in particular software process models. We aim to enable agile elements to be delimited, which should help companies, agile teams, and the research community gain a basic understanding of the interrelationships and dependencies of individual components of agile methods.
LENNA (Learning Emotions Neural Network Assisted): An Empathic Chatbot Designed to Study the Simulation of Emotions in a Bot and Their Analysis in a Conversation

Currently, most chatbots are unable to detect the emotional state of the interlocutor and respond according to the interlocutor’s emotional state. Over the last few years, there has been growing interest in empathic chatbots. In other disciplines aside from artificial intelligence, e.g., in medicine, there is growing interest in the study and simulation of human emotions. However, there is a fundamental issue that is not commonly addressed, and it is the design of protocols for quantitatively evaluating an empathic chatbot by utilizing the analysis of the conversation between the bot and an interlocutor. This study is motivated by the aforementioned scenarios and by the lack of methods for assessing the performance of an empathic bot; thus, a chatbot with the ability to recognize the emotions of its interlocutor is needed. The main novelty of this study is the protocol with which it is possible to analyze the conversations between a chatbot and an interlocutor, regardless of whether the latter is a person or another chatbot. For this purpose, we have designed a minimally viable prototype of an empathic chatbot, named LENNA, for evaluating the usefulness of the proposed protocol. The proposed approach uses Shannon entropy to measure the changes in the emotional state experienced by the chatbot during a conversation, applying sentiment analysis techniques to the analysis of the conversation. Once the simulation experiments were performed, the conversations were analyzed by applying multivariate statistical methods and Fourier analysis. We show the usefulness of the proposed methodology for evaluating the emotional state of LENNA during conversations, which could be useful in the evaluation of other empathic chatbots.
Conceptualization of the tabular representation of knowledge

Tabular representation is used as a basis for knowledge extraction. The structure of knowledge is built from a generalized concept to data structures that are used in applied software. The resulting formalizations allow for control of information in the form of natural language texts (regulatory documents), databases and spreadsheets of automated transport systems. The knowledge extracted from the tabular representation serves as the basis for decision-making and data mining.
Forest Fire Mapping using Normalized Burned Ratio and Cloud Computing to Calculate the Losses Incurred in Mount Lawu, Magetan Regency

Abstract Forest and land fires in Indonesia are one of the most common disasters. Over time, Indonesia’s forests have experienced a decline, one of which is the result of forest and land fires. Forest and land fires can occur due to temperatures in the dry season that continue to increase. Utilization of remote sensing technology can be used for mapping forest and fire areas, and can also be used as a reference for the government to carry out reforestation or reforestation processes in fire areas. The use of Multispectral Sensors in Sentinel 2 satellite imagery can be used to produce this research. This research takes a case study on Mount Lawu, Magetan Regency. Mount Lawu is one of the climbing tourist destinations that almost every year experiences forest and land fires. And the use of the Google Earth Engine platform that supports cloud computing to facilitate this research in accessing high data sources to process large geospatial data sets. For processing satellite images using the Normalized Burned Ratio (NBR) vegetation index processing algorithm. This research was conducted in 2018 and 2019. By using the Random Forest method, which is one of the applications of Machine Learning, it will produce several accurate results from the calculation results. The results of the calculation of the area of fire in 2018 were 284.88 hectares with a total loss of Rp 42,732,000.00 and in 2019 it was 146.03 hectares with a total loss of Rp 21,904,500. The results of this study are displayed on GEE Apps so that users can directly access the resulting data.