Reviewerly/exHarmony huggingface dataset:
Cognitive Radar Target Tracking Algorithm based on Waveform Selection

Aiming at the tracking problem of maneuvering target, a waveform library based on LFM signal is established from the perspective of waveform selection of cognitive radar. The maneuvering target was modeled by constant velocity model (CV) and constant acceleration model (CA), and the signal was filtered by interactive multi-model (IMM) based on Kalman filter. From the perspective of information theory, based on maximizing the mutual information between the target predicted state and the measured state, the optimal waveform library was established. The simulation results show that the waveform library can improve the tracking performance of radar, reduce the redundant waveform and reduce the computation.
Remote sensing: leveraging cloud IoT and AI/ML services

Artificial Intelligence/Machine Learning (AI/ML) services implemented at the tactical edge on multiple, distributed low power sensors can take advantage of Cloud IoT services and processes to learn in complex data environments supporting evolving mission tasks and continuous improvement of algorithms through Cloud automation and management. Remote sensors and their associated functionality must exhibit resilience against adversaries and deceptive techniques, and operate securely in all domains. Amazon Web Services (AWS) IoT Greengrass uses machine learning models that are built in the cloud and deployed locally on remote sensors and IoT devices. Limited datasets can be used to train models and be refined as more data is available. AWS SageMaker can be used for scene detection using Images and other signals of interest resulting in alerts and notifications from deployed sensors. The quality of machine learning models can be improved through captured data from IoT Greengrass being returned to the Cloud and processed by AWS SageMaker. Labeling of data can be streamlined through Cloud services such as Amazon SageMaker Ground Truth using auto-segment, automatic 3D cuboid snapping, and other automated labeling features. Using AWS Cloud security services in addition to AI/ML and IoT services provides additional advantages supporting identity and certificate management, sensor and data analytics, and intrusion detection and prevention. The number of low-cost sensors supporting Cloud-based IoT remote sensing using AI/ML algorithms at the edge continues to grow. Raspberry PI, NVIDIA Jetson Nano and others, equipped with appropriate sensor devices, are readily available for building a distributed remote sensing network and communicating with the Cloud. This paper provides a detailed description of components and the initial results of building a small distributed remote sensing network using NVIDIA Jetson Nano edge devices equipped with inexpensive acoustic and image sensors using IoT Greengrass and AWS SageMaker for detecting and identifying several target types. Additional Cloud services were used supporting monitoring and auditing, ensuring a secure and resilient operating environment.
A Citation Analysis of Theoretical Concept Reviews

In this article, we identify the theoretical concept review, a literature review that presents the theoretical concepts extracted from each primary study. We conduct a citation analysis that shows that, in general, theoretical concept reviews have been cited approximately 60% more than other kinds of reviews; moreover, theoretical concept reviews are compatible with every genre and goal of literature review. In our citation analysis, we create some new citation-related measures that can help other researchers in carrying out fruitful citation analyses. Based on our findings, we recommend that literature review authors should routinely present the elements of theory in their reviews since this is demonstrably valuable to readers.
Recognition for lateral faces using Neural Networks

Face recognition is most difficult and complicated technique. Recognition of lateral faces is very difficult compare with normal face recognition. Pattern recognition is mostly used in this system to recognise the lateral face patterns (LFP). Neural network is used to find the patterns and lateral face recognition can be done by this technique. After the many researches face recognition becomes difficulty for the various techniques based on their parameters. In this paper, the amalgamative lateral face recognition(ALFR) which is merged with machine learning and neural network features can be done by using synthetic dataset consists of 200 lateral faces. Performance shows the improved results of proposed technique.
Error analysis of undergraduate students’ writing performances: IELTS-based activities

As English as a Foreign Language (EFL) students, it is expected that they should have good ability in four skills of English, including writing skill. However, errors are still found in students’ written works. This paper examines the different types of writing errors made by 10 Indonesian undergraduate students who were enrolled in an IELTS preparation class. Descriptive qualitative research was employed in this study. The errors in the term papers were identified and classified accordingly. The students’ works were assessed based on four aspects: task achievement, cohesion and coherence, lexical resource, grammatical range, and accuracy. The result of this study showed that the highest percentage of students’ errors was in terms of lexical resource and grammatical range (accuracy), followed by three other common errors: singular or plural, word choice, and punctuation. Regarding to the task achievement, in Task 1, most of the students addressed the task but they didn't cover all the information needed. While in Task 2, some students presented clear opinions but with limited and inadequately developed ideas and gave no conclusion. Furthermore, for the results of cohesion and coherence in Task 1, most students showed good logical sequence and overall progression but with faulty cohesion devices. However in Task 2, some students presented either incoherent or illogical ideas or information. These results contribute as fruitful insights for language learners who want to enhance the IELTS comprehension, particularly writing performance.
Robust calibration method for microscopic vision systems

In order to improve the stability of the calibration method based on a single parallel plane, we propose a robust calibration method based on the calculation of traditional homography matrix and Gaussian imaging model. This method first uses a convenient and accurate method to calculate the physical focal length of the entire microscope optical system, and then uses the Gaussian imaging model to calculate the effective focal length and intrinsic matrix of the camera. Then, the camera’s extrinsic parameters can be easily calculated from the homography matrix. Finally, all camera parameters can be optimized along with the lens distortion through a nonlinear optimization process. In this method, only two images of the parallel calibration plate need to be captured to complete the calibration. The proposed method overcomes the problem of instability of the existing microscope calibration methods and significantly improves the robustness and accuracy of the whole calibration process.
Intelligent Software Agent in E-Health System: - Review

The field of medical care is characterized by complexity, dynamism, and variety. Different challenges occur in healthcare (increasing cost of care, population growth and lack of caregivers). For that, intelligent applications with Agent Technology can provide better health care than the conventional medical system. In fact, the characteristics of Intelligent Agents (proactivity, sociability, autonomy) are a good choice to solve many problems in this regard, since the key success parameter it was the natural mapping for real-life medical issues into electronic life. The design of Multi-agent architecture will effectively paradigm the heterogeneous, distributed, and independent health treatment systems. The multi-agent systems have been used of individual healthcare processes such as a knowledge-depend medical system for complex, multi-parties depend on systems such as a total healthcare where The applied from multi-agent systems(mas) in health treatment sector also has been opened avenues for discovering innovative applications such as socialized and personalized health care models. Therefore, when creating an agent-depend simulation, one of the most important questions to consider is how to model the decision decision-making operations for the agents in the model. In the literature, the wide There are a variety of agent decision-making models available, all of them motivated by multiple goals and study questions. The research explains the advances made so far in the development of regulatory frameworks for deliberative autonomous software in a partially observable environment. In this paper, we display an overview for some of an agent that has attracted attention (Beliefs Desires and Intentions (BDI) and its derivatives such as Extensible Beliefs Desires and Intentions (EBDI), Beliefs-Desires-Obligations-Intentions (BOID)).).
Road Constrained Labeled Multi Bernoulli Filter based on PDF Truncation for Multi-Target Tracking

In this paper, road constrained filtering is applied to labeled multi Bernoulli (LMB) filter using PDF truncation in multi-road environment. In target tracking systems with road map information, road constraints can effectively improve the estimation performance. To apply multiple road constraints information to the tracking filter, all constraints should not be applied simultaneously and only one should be selected for each estimated trajectory. Then, probability density function (PDF) truncation is conducted which is a constrained filtering technique for inequality constraints. To verify the constrained filtering technique to LMB filter, simulations for multi-target tracking in cluttered environments are carried out. The simulation result shows that the proposed method bounded estimated trajectories on the road effectively and reduced OSPA error.
A COVID-19 news coverage mood map of Europe

We present a COVID-19 news dashboard which visualizes sentiment in pandemic news coverage in different languages across Europe. The dashboard shows analyses for positive/neutral/negative sentiment and moral sentiment for news articles across countries and languages. First we extract news articles from news-crawl. Then we use a pre-trained multilingual BERT model for sentiment analysis of news article headlines and a dictionary and word vectors -based method for moral sentiment analysis of news articles. The resulting dashboard gives a unified overview of news events on COVID-19 news overall sentiment, and the region and language of publication from the period starting from the beginning of January 2020 to the end of January 2021.
The future of print media in the Republic of Serbia in the digital age: Media-sociological aspect

The constant decline in circulation, audience and print revenue, the development of a new culture of information, with the concurrent strengthening of online media, have led to the point where the transformation of print media has become inevitable. Therefore, in theoretical and professional discussions, the key question is how to respond to all changes and ensure the survival of print media outlets. In order to investigate this issue, a study based on the empirical method and the procedure of non-experimental research was conducted, which included five daily newspaper newsrooms in Serbia. The results show that new journalistic competencies are still present on a small scale in most newsrooms, that they are characterized by traditional work organization, as well as that they do not sufficiently involve new media professionals. These are also three key elements that hinder the transformation of print media and their adaptation to the digital age.
Using Blockchain in University Management Systems

lockchain stores a series of transactions in form of a sequence of linked blocks. Hence, the concept of ledger is easily maintained. Transactions and interactions that take place among participants accessing the distributed and decentralized blockchain network are holding through ledger. In a student management system (SMS), vital information can be highly shared and well protected at the same time. This paper proposes a model for using blockchains to implement fully functional SMS that maintains students’ records, course registrations record and student marks. The proposed model adds more security via the use of hashing and data readily available with decentralized data storage. In addition, the use of ledger-based system to maintain SMS data introduces reliable and highly trusted model.
A Rank based Adaptive Mutation in Genetic Algorithm

Traditionally Genetic Algorithm has been used for optimization of unimodal and multimodal functions.Earlier researchers worked with constant probabilities of GA control operators like crossover, mutation etc. for tuning the optimization in specific domains.Recent advancements in this field witnessed adaptive approach in probability determination.In Adaptive mutation primarily poor individuals are utilized to explore state space, so mutation probability is usually generated proportionally to the difference between fitness of best chromosome and itself (f MAX -f).However, this approach is susceptible to nature of fitness distribution during optimization.This paper presents an alternate approach of mutation probability generation using chromosome rank to avoid any susceptibility to fitness distribution.Experiments are done to compare results of simple genetic algorithm (SGA) with constant mutation probability and adaptive approaches within a limited resource constraint for unimodal, multimodal functions and Travelling Salesman Problem (TSP).Measurements are done for average best fitness, number of generations evolved and percentage of global optimum achievements out of several trials.The results demonstrate that the rank-based adaptive mutation approach is superior to fitness-based adaptive approach as well as SGA in a multimodal problem space.
Comparison of elliptical unwrapping and frontal projection for off-angle iris normalization

Iris recognition is one of the most accurate biometric recognition techniques, however off-angle iris recognition has yet to have an established comprehensive recognition framework. This is due to the difficulties in the recognition of off-angle iris image inconsistencies within the iris patterns when gaze deviations are present. In this work, we investigate different iris normalization techniques and compare their performance. The two methods under investigation include elliptical normalization and circular normalization after frontal projection of off-angle iris recognition. Elliptical normalization samples the iris texture using elliptical segmentation parameters: 𝑥, 𝑦, 𝑟<sub>1</sub> , 𝑟<sub>2</sub> , &theta; where 𝑥, 𝑦 are coordinates, 𝑟<sub>1</sub>, 𝑟<sub>2</sub> are the radius, and &theta; is the orientation. Also, when investigating circular unwrapping, we will be using the ellipse segmentation parameters to estimate the gaze deviation. The image will be projected back to a frontal view using perspective transformation. Then, we segment the transformed image and normalize using the circular parameters: 𝑥, 𝑦, 𝑟 where 𝑥, 𝑦 are coordinates and r is the radius. We further investigate if: (i) elliptical normalization or circular unwrapping recognition performance is higher, and (ii) if the two segmentation methods in circular unwrapping increase the recognition efficiency
The Influence of Augmented Reality-Based Learning Media on the Students’ Achievement of Mathematics

This study was aimed to determine the effectiveness of using augmented reality based learning media on mathematics competencies in elementary school.This study used a pre-experimental approach in learning geometry with the onegroup pretest-posttest design and the research subject was selected randomly.Before being given the treatment, the selected group of students was given a pretest to find out the students' prior knowledge about geometry.The selected group was taught about geometry by using augmented reality-based learning media and given posttest afterwards.The instrument used for data collection in this study was 40 valid and reliable items.Furthermore, from the posttest and pretest scores obtained a gain score using paired sample t test to determine whether there were differences in knowledge competencies before and after using augmented reality-based learning media.The findings of the study revealed that the average posttest result was 87.89 and the pretest average result was 82.73.The data was obtained the calculated value of 9.198, with df = 31 and α = 0.05, the t table value was 2.040.This results showed that there are significant differences in the students' mathematical competencies before and after using augmented reality based learning media.Since the average posttest is greater than the average pretest, it can be concluded that augmented reality-based learning media is effective to be applied as a learning media for elementary school.
Application of multidomain data fusion, machine learning and feature learning paradigms towards enhanced image-based SAR class vehicle recognition

Deep convolutional neural networks (CNNs) provide the sensing and detection community with a discriminative, machine learning based approach for classifying images of objects. However, one of the largest limitations for deep CNN image classifiers is the need for extensive training data for a variety of appearances of class objects. While current methods such as GAN data augmentation, noise-perturbation, and rotation or translation of images can allow CNNs to better associate convolved features to ones similar to a learned image class, many fail to provide new context of ground truth information associated with each object class. To expand the association of new convolved feature examples with image classes within CNN training datasets, we propose a feature learning and training data enhancement paradigm via a multi-sensor domain data augmentation algorithm. This algorithm uses a mutual information, merit-based feature selection subroutine to iteratively select SAR object features that most correlate to each sensor domain’s class image objects. It then re-augments these features into the opposite sensor domain’s feature set via a highest mutual information, cross sensor domain image concatenation function. This augmented set then acts to retrain the CNN to recognize new cross domain class object features that each respective sensor domain’s network was not previously exposed to. Our experimental results using T60- class vs T70-class SAR object images from both the MSTAR and MGTD dataset repositories demonstrated an increase in classification accuracy from 88% and 61% to post-augmented cross-domain dataset training of 93.75% accuracy for the MSTAR, MGTD and subsequent fused datasets, respectively.
ALUE: Arabic Language Understanding Evaluation

The emergence of Multi-task learning (MTL)models in recent years has helped push thestate of the art in Natural Language Un-derstanding (NLU). We strongly believe thatmany NLU problems in Arabic are especiallypoised to reap the benefits of such models. Tothis end we propose the Arabic Language Un-derstanding Evaluation Benchmark (ALUE),based on 8 carefully selected and previouslypublished tasks. For five of these, we providenew privately held evaluation datasets to en-sure the fairness and validity of our benchmark.We also provide a diagnostic dataset to helpresearchers probe the inner workings of theirmodels.Our initial experiments show thatMTL models outperform their singly trainedcounterparts on most tasks. But in order to en-tice participation from the wider community,we stick to publishing singly trained baselinesonly. Nonetheless, our analysis reveals thatthere is plenty of room for improvement inArabic NLU. We hope that ALUE will playa part in helping our community realize someof these improvements. Interested researchersare invited to submit their results to our online,and publicly accessible leaderboard.
Extracting Synonyms from Bilingual Dictionaries

We present our progress in developing a novel algorithm to extract synonyms from bilingual dictionaries. Identification and usage of synonyms play a significant role in improving the performance of information access applications. The idea is to construct a translation graph from translation pairs, then to extract and consolidate cyclic paths to form bilingual sets of synonyms. The initial evaluation of this algorithm illustrates promising results in extracting Arabic-English bilingual synonyms. In the evaluation, we first converted the synsets in the Arabic WordNet into translation pairs (i.e., losing word-sense memberships). Next, we applied our algorithm to rebuild these synsets. We compared the original and extracted synsets obtaining an F-Measure of 82.3% and 82.1% for Arabic and English synsets extraction, respectively.
Opportunities And Restrictions Of Online Communication In Higher Educational System

The development of information and communication technologies, the definition of the content and direction of current policy in education, the formation of feedback channels with students -all these are the ways to increase the effectiveness of modern higher education.The partial transition of universities to the online environment is the challenge of our time.On the one hand, modern communications provide new opportunities for interaction between teachers and students.On the other hand, face the formalism and unwillingness (technical, moral, etc.) of their full application.The research aims to analyze the current state and potential of online communication between teachers and students in the higher education system.The article discusses the features of the use of new communication technologies in university teachers' activities in the interaction with students.The possibilities of modern means of communication for effective cooperation and implementation of the educational process are investigated.The problems and possibilities of using online education in crises are considered.The readiness of teachers to interact with students through modern communication technologies is analyzed.The satisfaction of students and teachers with online interaction and distance education is revealed using survey methods.The conclusions outline the problem and promising aspects of modern online communications, their impact on increasing distance education efficiency, and predicting possible options for its further use.
Cryptography: A Combination of Caesar and Affine Cipher to Conceal the Message

Advances in technology make it easy for humans to communicate with each other. One of them is by sending messages. However, the confidentiality of messages can be hacked by third parties. Therefore, it is necessary to secure the messages sent so that confidentiality can be maintained. One way that can be done to maintain message security is cryptography. Classical cryptography has several types of algorithms including caesar cipher and affine cipher. The purpose of this study is to combine caesar cipher and affine cipher as one technique that can be used to secure messages. This combination is done by changing the plaintext into ciphertext 1 through caesar encryption. Ciphertext 1 is encrypted by affine caesar to produce ciphertext 2. Ciphertext 2 is the message to be sent. Then to change to original message ciphertext 2 is decrypted by affine chiper so that it gets plaintext 1. Then plaintext 1 is decrypted by caesar chiper so that it gets plaintext 2. Plaintext 2 is the message that should be received. The result of this study are a combination of caesar cipher and affine cipher can secure a message sent.