Reviewerly/exHarmony huggingface dataset:
Information System Prototyping of Strawberry Maturity Stages using Arduino Uno and TCS3200

Strawberry is one of the commodities of subtropical fruits growing in Indonesia. It has a high economic value, attractive appeal in a red and fresh sweet fruit. The color of a strawberry shows its maturity stages, and the strawberry maturity stages affect postharvest quality. The research aimed to design and implement a tool to approve and apply the maturity stages of strawberries using Arduino Uno and display the information on the web. The method included needs analysis and analysis of work, and the design consisted of hardware design and network design. The testing consisted of TCS3200 sensor testing, LCD testing, servo motor testing, and WEB testing. The results of this study found that when the strawberry fruit was declared mature by the TCS3200 sensor as the color detection sensor, the LCD would display the texts, and the servo would open. The system on the WEB would display the total information of the sorted strawberries.
Beyond the Surveys: Qualitative Analysis from the Academic Reading Format International Study (ARFIS)

The Academic Reading Format International Study (ARFIS) collected data from more than 21,000 university students in 33 countries regarding their reading format (print or electronic) preferences and behaviors when engaging with academic texts during a three-year period. Quantitative analysis shows a consistent preference for print reading among most students worldwide. This paper presents new findings from our qualitative analysis of students’ survey comments, addressing the questions: Why do students prefer to read in print or electronically? What factors affect their decisions to read a text in their less preferred format? Are there behavioral patterns that will assist librarians and educators when deciding upon format policies?
A Hybrid Algorithm Framework with Learning and Complementary Fusion Features for Whale Optimization Algorithm

It has been observed that the structure of whale optimization algorithm (WOA) is good at exploiting capability, but it easily suffers from premature convergence. Hybrid metaheuristics are of the most interesting recent trends for improving the performance of WOA. In this paper, a hybrid algorithm framework with learning and complementary fusion features for WOA is designed, called hWOAlf. First, WOA is integrated with complementary feature operators to enhance exploration capability. Second, the proposed algorithm framework adopts a learning parameter <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>l</mml:mi><mml:mi>p</mml:mi></mml:mrow></mml:math> according to adaptive adjustment operator to replace the random parameter <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mi>p</mml:mi></mml:math>. To further verify the efficiency of the hWOAlf, the DE/rand/1 operator of differential evolution (DE) and the mutate operator of backtracking search optimization algorithm (BSA) are embedded into WOA, respectively, to form two new algorithms called WOA-DE and WOA-BSA under the proposed framework. Twenty-three benchmark functions and six engineering design problems are employed to test the performance of WOA-DE and WOA-BSA. Experimental results show that WOA-DE and WOA-BSA are competitive compared with some state-of-the-art algorithms.
Experimental investigation of linear-entropy-based uncertainty relations in all-optical systems

We report an all-optical experiment to investigate the uncertainty relations based on linear entropy. First we prepare two kinds of biphoton states initially, i.e., Bell-like states and Werner states. Then we perform projective measurements on one subsystem over a complete or incomplete set of mutually unbiased bases; the corresponding measurement probabilities can be directly determined by the photon coincidence counting. Together with the initial state and the postmeasurement states of the system available by quantum state tomography, linear-entropy-based uncertainty relations can be fully verified. It is shown that the experimental results agree very well with the theoretical predictions. Also, it is revealed that entanglement of the initial biphoton states is anticorrelated with the total of uncertainties. Our experimental results could not only deepen the understanding of uncertainty relations, but also facilitate potential applications to quantum technologies.
The Effects Of Mobile Computer Supported Collaborative Learning to Improve Problem Solving and Achievements

This research aims to prove whether mobile Computer-Supported Collaborative Learning (mCSCL) is better at improving learning outcomes to solve problems than mobile Computer-Supported Individual Learning (mCSIL) based on the student's Self Regulated Learning level. Data analysis techniques in this study used SPSS programs with two-way variance analysis. The research subjects involved 140 Harapan Bangsa Academy students. The results of the analysis found that mCSCL has an average of 67,071 provides better learning outcomes for problem-solving than mCSIL, which is 63,414. Also, this study found that students with higher Self Regulated Learning (SRL) had better learning achievement than students with lower Self Regulated Learning that is, 66.5 compared to 63.986. Furthermore, the test results also obtained Fab = 3,326 with Sig. = 0.07 then H0AB is accepted. This matter means the mCSCL and mCSIL learning strategies provide equally good learning achievements for students who have high or low Self Regulated Learning. The novelty of this study is that the mobile Computer-Supported Collaborative Learning strategy will be more effective in improving problem-solving learning outcomes if students have high Self Regulated Learning.
Robot Risk-Awareness by Formal Risk Reasoning and Planning

This letter proposes a formal robot motion risk reasoning framework and develops a risk-aware path planner that minimizes the proposed risk. While robots locomoting in unstructured or confined environments face a variety of risk, existing risk only focuses on collision with obstacles. Such risk is currently only addressed in ad hoc manners. Without a formal definition, ill-supported properties, e.g. additive or Markovian, are simply assumed. Relied on an incomplete and inaccurate representation of risk, risk-aware planners use ad hoc risk functions or chance constraints to minimize risk. The former inevitably has low fidelity when modeling risk, while the latter conservatively generates feasible path within a probability bound. Using propositional logic and probability theory, the proposed motion risk reasoning framework is formal. Building uponauniverse of risk elements of interest, three major risk categories, i.e. locale-, action-, and traverse-dependent, are introduced. A risk-aware planner is also developed to plan minimum risk path based on the newly proposed risk framework. Results of the risk reasoning and planning are validated in physical experiments in a real-world unstructured or confined environment. With the proposed fundamental risk reasoning framework, safety of robot locomotion could be explicitly reasoned, quantified, and compared. The risk-aware planner finds safe path in terms of the newly proposed risk framework and enables more risk-aware robot behavior in unstructured or confined environments.
Understanding the effectiveness of adaptive guidance for narrative visualization

We study the effectiveness of adaptive guidance at helping users process textual documents with embedded visualizations, known as narrative visualizations. We do so by leveraging eye tracking to analyze in depth the effect that adaptations meant to guide the user's gaze to relevant parts of the visualizations has on users with different levels of visualization literacy. Results indicate that the adaptations succeed in guiding attention to salient components of the narrative visualizations, especially by generating more transitions between key components of the visualization (i.e., datapoints, labels and legend). We also show that the adaptation helps users with lower levels of visualization literacy to better map datapoints to the legend, which leads in part to improved comprehension of the visualization. These findings shed light on how adaptive guidance helps users with different levels of visualization literacy, informing the design of personalized narrative visualizations.
Data Mining in Clinical Trial Text: Transformers for Classification and Question Answering Tasks

This research on data extraction methods applies recent advances in natural language processing to evidence synthesis based on medical texts.Texts of interest include abstracts of clinical trials in English and in multilingual contexts.The main focus is on information characterized via the Population, Intervention, Comparator, and Outcome (PICO) framework, but data extraction is not limited to these fields.Recent neural network architectures based on transformers show capacities for transfer learning and increased performance on downstream natural language processing tasks such as universal reading comprehension, brought forward by this architecture's use of contextualized word embeddings and self-attention mechanisms.This paper contributes to solving problems related to ambiguity in PICO sentence prediction tasks, as well as highlighting how annotations for training named entity recognition systems are used to train a high-performing, but nevertheless flexible architecture for question answering in systematic review automation.Additionally, it demonstrates how the problem of insufficient amounts of training annotations for PICO entity extraction is tackled by augmentation.All models in this paper were created with the aim to support systematic review (semi)automation.They achieve high F1 scores, and demonstrate the feasibility of applying transformer-based classification methods to support data mining in the biomedical literature.
WGAN-E: A Generative Adversarial Networks for Facial Feature Security

Artificial intelligence technology plays an increasingly important role in human life. For example, distinguishing different people is an essential capability of many intelligent systems. To achieve this, one possible technical means is to perceive and recognize people by optical imaging of faces, so-called face recognition technology. After decades of research and development, especially the emergence of deep learning technology in recent years, face recognition has made great progress with more and more applications in the fields of security, finance, education, social security, etc. The field of computer vision has become one of the most successful branch areas. With the wide application of biometrics technology, bio-encryption technology came into being. Aiming at the problems of classical hash algorithm and face hashing algorithm based on Multiscale Block Local Binary Pattern (MB-LBP) feature improvement, this paper proposes a method based on Generative Adversarial Networks (GAN) to encrypt face features. This work uses Wasserstein Generative Adversarial Networks Encryption (WGAN-E) to encrypt facial features. Because the encryption process is an irreversible one-way process, it protects facial features well. Compared with the traditional face hashing algorithm, the experimental results show that the face feature encryption algorithm has better confidentiality.
A Hybrid Tabu Search and 2-opt Path Programming for Mission Route Planning of Multiple Robots under Range Limitations

The application of an unmanned vehicle system allows for accelerating the performance of various tasks. Due to limited capacities, such as battery power, it is almost impossible for a single unmanned vehicle to complete a large-scale mission area. An unmanned vehicle swarm has the potential to distribute tasks and coordinate the operations of many robots/drones with very little operator intervention. Therefore, multiple unmanned vehicles are required to execute a set of well-planned mission routes, in order to minimize time and energy consumption. A two-phase heuristic algorithm was used to pursue this goal. In the first phase, a tabu search and the 2-opt node exchange method were used to generate a single optimal path for all target nodes; the solution was then split into multiple clusters according to vehicle numbers as an initial solution for each. In the second phase, a tabu algorithm combined with a 2-opt path exchange was used to further improve the in-route and cross-route solutions for each route. This diversification strategy allowed for approaching the global optimal solution, rather than a regional one with less CPU time. After these algorithms were coded, a group of three robot cars was used to validate this hybrid path programming algorithm.
A Novel Route Planning Method of Fixed-Wing Unmanned Aerial Vehicle Based on Improved QPSO

This paper proposes a quick and accurate method based on an improved quantum-behaved particle swarm optimization (QPSO) algorithm for route planning of fixed-wing unmanned aerial vehicle (UAV).To overcome the deficiencies of local optima and slow global convergence speed, a novel strategy of particle dimension search is proposed in the QPSO algorithm.It is implemented by transforming original evaluation function into evaluation function of waypoint to more easily escape from local optima and accelerate global convergence speed.In addition, an efficient pretreatment technology for the initial trajectory is set to shorten the calculation time of route planning.Compared with other representative route planners, the comparison results indicate that the proposed route planner is more effective and feasible, which can take on faster convergence speed and better global search ability.The proposed route planner can provide a valuable reference for the route planning of fixed-wing UAVs in different environments.
Semi-Supervised Speaker Adaptation for End-to-End Speech Synthesis with Pretrained Models

Recently, end-to-end text-to-speech (TTS) models have achieved a remarkable performance, however, requiring a large amount of paired text and speech data for training. On the other hand, we can easily collect unpaired dozen minutes of speech recordings for a target speaker without corresponding text data. To make use of such accessible data, the proposed method leverages the recent great success of state-of-the-art end-to-end automatic speech recognition (ASR) systems and obtains corresponding transcriptions from pretrained ASR models. Although these models could only provide text output instead of intermediate linguistic features like phonemes, end-to-end TTS can be well trained with such raw text data directly. Thus, the proposed method can greatly simplify a speaker adaptation pipeline by consistently employing end-to-end ASR/TTS ecosystems. The experimental results show that our proposed method achieved comparable performance to a paired data adaptation method in terms of subjective speaker similarity and objective cepstral distance measures.
Blind Estimation of Interleaver Parameter With a Limited Number of Data

In non-cooperative contexts, one needs to estimate communication parameters by using collected data without any prior information. Particularly, when collecting only a limited number of data, estimation becomes a more challenging task. This paper presents a method for estimation of interleaver parameter when only a limited number of collected data are available. We first create additional data by combining a limited number of collected data. We then investigate the rank deficiency of the matrices composed of the collected and additionally-created data. Finally, we estimate the interleaver parameter by using the difference of average rank deficiencies. Through computer simulations, we validate the proposed method in terms of detection probability and the number of false alarms.
Modulation recognition in maritime multipath channels: A blind equalization-aided deep learning approach

Modulation recognition has been long investigated in the literature, however, the performance could be severely degraded in multipath fading channels especially for high-order Quadrature Amplitude Modulation (QAM) signals. This could be a critical problem in the broadband maritime wireless communications, where various propagation paths with large differences in the time of arrival are very likely to exist. Specifically, multiple paths may stem from the direct path, the reflection paths from the rough sea surface, and the refraction paths from the atmospheric duct, respectively. To address this issue, we propose a novel blind equalization-aided deep learning (DL) approach to recognize QAM signals in the presence of multipath propagation. The proposed approach consists of two modules: A blind equalization module and a subsequent DL network which employs the structure of ResNet. With predefined searching step-sizes for the blind equalization algorithm, which are designed according to the set of modulation formats of interest, the DL network is trained and tested over various multipath channel parameter settings. It is shown that as compared to the conventional DL approaches without equalization, the proposed method can achieve an improvement in the recognition accuracy up to 30% in severe multipath scenarios, especially in the high SNR regime. Moreover, it efficiently reduces the number of training data that is required.
Prospects for the Introduction of Artificial Intelligence Technologies in Higher Education

At present, comprehensive research from various points of view (social, economic, ethical, and legal) on the implementation of artificial intelligence technologies in various spheres of society's life is of particular relevance. It is important to identify opportunities and threats, to determine the limits of the use of artificial intelligence in practice. The article is devoted to the introduction of artificial intelligence technologies in higher education. Prospective directions of using artificial intelligence in higher education are considered and analyzed. The authors conclude that artificial intelligence is not a competitor to the teaching staff, either in teaching or in evaluating students' knowledge. Artificial intelligence is an auxiliary, but valuable tool that can perform and improve a large number of different operations performed at the University, help in organizing an effective educational process and building the necessary communications. Effective use of AI technologies in higher education will allow you to select the most optimal learning strategy adapted to the individual abilities and needs of students and the needs of the labor market.
Reversible Data Hiding Using Improved Gradient Based Prediction and Adaptive Histogram Bin Shifting

In this paper, a novel reversible data hiding method is proposed using an improved gradient based prediction. The gradients are computed across horizontal, vertical, diagonal and anti-diagonal directions using 5 × 5 neighborhood. The final predicted value is obtained as a weighted average of two linearly predicted values in the directions of the smallest two gradients. An adaptive prediction error histogram bin shifting method is also proposed to insert either 1 bit or 2 bits in the prediction error of a pixel. It is experimentally observed that the proposed reversible data hiding technique using an improved gradient based prediction and adaptive prediction error histogram bin shifting outperforms several other existing techniques.
Smart Contracts for supply chain applicable to Smart Cities daily operations

In Smart Cities, an era of technology and on-demand offerings, being able to have a signed contract in the least amount of time possible can significantly benefit business operations. This project focuses on the Document of Understanding (DOU) contract oriented to supply chain operations, which forms the basis for the relationship between a consumer service and the provider of that service. The sign off of this process is currently too long; opening an opportunity to apply Blockchain as a solution. For this task, we have used local resources, applying design thinking and agile practices to create a local Blockchain Ledger. Consequently, we created a proof of concept Blockchain demo, which holds the complete detailed history of the agreement, with immutable transactions and transparency, adding security and privacy of the participant's information. With this demo, we registered the time needed to have the DOU contract signed off by everyone involved in the process, and it was drastically improved. The innovative contribution of this project is the application of Blockchain to our business activities, improving business operations, and giving us a real-time view of all the information. As a result, when combining our work methods with the latest technology, we have all-around improvements in business operations. Now with a successful trial run of the application, we can confidently apply smart contracts in day to day activities in a Smart City; it can be replicated in other areas that manage sensitive information and financial reports.
Conditions for creating a balance between learning styles on the example of the material of the discipline “Ecological Chemistry and Environmental Monitoring”

Aspects of learning styles modelled on R. Felder and B. Soloman are discussed in detail. The results of the study of the educational advantages of students of Kryvyi Rih State Pedagogical University of the speciality “014 Secondary education (Chemistry)” are presented. It is shown that the majority of students in the group are those who study visually, sensitively, actively and consistently. According to studied features of the student’s groups didactic material on the theme “Ecological Chemistry of the Lithosphere” of the content module “Ecological Chemistry of Environmental Objects” was elaborated and its role and place in the structure of the educational process determined. Forms of work that involve the use of different cognitive functions are described and therefore contribute to their balanced development. It allows a person to be flexible in the unrestrained development of technological progress, to be open to different ways of obtaining information and perceiving it without resistance and stress.
Vehicle detection from multi-modal aerial imagery using YOLOv3 with mid-level fusion

Target detection is an important problem in remote-sensing with crucial applications in law-enforcement, military and security surveillance, search-and-rescue operations, and air traffic control, among others. Owing to the recently increased availability of computational resources, deep-learning based methods have demonstrated state-of- the-art performance in target detection from unimodal aerial imagery. In addition, owing to the availability of remote-sensing data from various imaging modalities, such as RGB, infrared, hyper-spectral, multi-spectral, synthetic aperture radar, and lidar, researchers have focused on leveraging the complementary information offered by these various modalities. Over the past few years, deep-learning methods have demonstrated enhanced performance using multi-modal data. In this work, we propose a method for vehicle detection from multi-modal aerial imagery, by means of a modified YOLOv3 deep neural network that conducts mid-level fusion. To the best of our knowledge, the proposed mid-level fusion architecture is the first of its kind to be used for vehicle detection from multi-modal aerial imagery using a hierarchical object detection network. Our experimental studies corroborate the advantages of the proposed method.