Reviewerly/exHarmony huggingface dataset:
An Analysis of the Ingredients for Learning Interpretable Symbolic Regression Models with Human-in-the-loop and Genetic Programming

Interpretability is a critical aspect to ensure a fair and responsible use of machine learning (ML) in high-stakes applications. Genetic programming (GP) has been used to obtain interpretable ML models because it operates at the level of functional building blocks: if these building blocks are interpretable, there is a chance that their composition (i.e., the entire ML model) is also interpretable. However, the degree to which a model is interpretable depends on the observer. Motivated by this, we study a recently-introduced human-in-the-loop system that allows the user to steer GP’s generation process to their preferences, which shall be online-learned by an artificial neural network (ANN). We focus on the generation of ML models as analytical functions (i.e., symbolic regression) as this is a key problem in interpretable ML, and propose a two-fold contribution. First, we devise more general representations for the ML models for the ANN to learn upon, to enable the application of the system to a wider range of problems. Second, we delve into a deeper analysis of the system’s components. To this end, we propose an incremental experimental evaluation, aimed at (1) studying the effectiveness by which an ANN can capture the perceived interpretability for simulated users, (2) investigating how the GP’s outcome is affected across different simulated user feedback profiles, and (3) determining whether humans participants would prefer models that were generated with or without their involvement. Our results pose clarity on pros and cons of using a human-in-the-loop approach to discover interpretable ML models with GP.
Q&amp;A Aftab Khan, a career in geophysics

Abstract Sue Bowler talks to Aftab Khan, who recently celebrated his 90th birthday, about his career in geophysics, from the origins of the modern subject, through the exploration of the African Rift Valley and the steps to a nuclear test ban treaty
Fault prediction of unmanned aerial vehicles based on entropy weight fusion and temporal graph convolutional network with non-Euclidian structure

Abstract Predicting weak and hidden faults in unmanned aerial vehicles (UAVs) is challenging due to their variable operation conditions and complex mechanisms. Conventional neural network models process the multisensory data in the form of Euclidean structure, the intrinsic connections among the individual data points are easy to be disregarded. Additionally, multisensory data are always directly fed into the model without adequately considering the importance or contribution of each sensor. Hence, an UAV fault prediction method is proposed by combining entropy weight fusion with a temporal graph convolutional network (GCN) to address the above problems. Firstly, the importance of multisensory data of UAVs are evaluated by each entropy value, and the multisensory data fusion is further realized by multiplying corresponding signal and entropy weight. Secondly, the multisensory data combined with fusion data are transferred together into graph-structure by adjacent matrix based on the node connection between different sensor data. Finally, the graph-structure data with non-Euclidian distance properties are input into temporal GCN to both capture the spatial and temporal relationship of the data, achieving better fault prediction results of UAVs. It is demonstrated that the proposed method is both applicable and superior in characterizing and predicting fault time series information of UAVs through parameter analysis and comparison studies with various existing algorithms.
METHODOLOGY FOR IDENTIFYING CURRICULAR WEAKNESSES FROM ENADE COURSE REPORT: performance of a Physics degree in ENADE 2021

It is understood that the National Student Performance Exam (ENADE) makes it possible to analyze curricular weaknesses capable of supporting the improvement of curricular structures.A methodology is proposed to identify these weaknesses per observation unit (OU), based on the ENADE course report.The method is exemplified, focusing on 27 objective questions (64% of the grade) from the specific component (CE) of ENADE 2021 for a degree in Physics, in which the trainees showed themselves committed to the Exam (74% stayed more than 3 hours).Four items were disregarded, due to a discrimination rate of less than 20%, consolidating the analysis into 26 distractors that attracted at least 20% of respondents.Three items are exemplified that consolidate weaknesses linked to basic mechanics and other items that materialized difficulties in Practice in Physics Teaching and Inclusion are highlighted.It is understood that this systematic analysis can contribute as a subsidy in studies of curricular changes in Undergraduate courses.
MEASUREMENT OF EMPLOYEE INFORMATION SECURITY AWARENESS: CASE STUDY AT FINANCIAL INSTITUTION

The lack of awareness regarding information security among employees in financial institutions can have detrimental impacts on both customers and the institution itself, both financially and in terms of trust. Therefore, this research aims to assess the information security awareness at PT XYZ, a financial institution, in order to identify the existing level of awareness, which will be used to provide recommendations. The method applied in this research is quantitative, using a questionnaire as a tool for data collection and distribution with a voluntary sampling technique among PT XYZ employees concerning their awareness of information security. The assessment of information security awareness covers 8 specific aspects, with 7 of them drawing sources from HAIS-Q and another 1 referring to the KAMI Index, using the Analytic Hierarchy Process (AHP) for weighting each area. The total number of respondents participating in this research is 52. The research results affirm that PT XYZ employees have a positive awareness of information security, indicating that there are no urgent actions needed at present. However, there are specific areas with potential for improvement, hence recommendations are provided to enhance and sustain information security awareness among employees.
Semi-device-independent quantum random number generator with a broadband squeezed state of light

Abstract Random numbers are a basic ingredient of simulation algorithms and cryptography, and play a significant part in computer simulation and information processing. One prominent feature of a squeezed light is its lower fluctuation and more randomness in a pair of orthogonal oriented quadratures, thus it prompts a significant application in not only quantum information and quantum precision measurement but also an excellent entropy source for true random number generation. Here we report a generation of a high-efficiency semi-device-independent quantum random number based on a broadband squeezed light, where a reliable randomness source is unnecessary and a noisy local oscillator is allowed for homodyne detection. The equivalent generation of private random bits is at a rate of 580.7 Mbps. In addition, the use of squeezed light at 1.3 μm enables the transmission of entropy sources and local oscillators at the metropolitan scale, thus expanding the potential applications of quantum random number generators based on non-classical state of light.
Patenting artificial intelligence: the High Court’s decision in <i>Emotional Perception</i>

Abstract In Emotional Perception, the EWHC held that an artificial neural network implementing a media recommendation system did not engage the ‘computer program’ exclusion under the Patents Act 1977. Following the ruling, the UK Intellectual Property Office suspended its existing rules on AI patents and issued fresh guidance on the issue.
Training children's cognitive functions using eye-tracking technology

&lt;p style="text-align: justify;"&gt;The active development of eye-tracking, that is, recording of eye movements, gives opportunities for the creation of new approaches to the improving of cognitive functions and learning. Eye-tracking based methods can be useful not only for children with impaired motor and speech functions, but also for children with attention disorders, since gaze control is closely related to attention. Feedback from the gaze accuracy can improve the effectiveness of attention training techniques. In this paper, we describe studies of the effectiveness of the use of correctional techniques based on eye-tracking in infants (healthy group and groups with various risks for cognitive impairment) and in children with developmental disorders (autism, Rett syndrome, attention deficit hyperactivity disorder). In almost all of the studies reviewed, positive effects of training were observed, i. e. both the productivity of the tasks performance and sustained attention were improved. A number of studies have revealed the transfer of the training effect to untrained functions, which is an important indicator of the effectiveness of correction techniques. All this indicates the perspective of development in this direction. However, much work is to be done, such as selecting and creating the most effective tasks, determining the optimal frequency and duration of training for different clinical groups.&lt;/p&gt;
Path tracking control for autonomous vehicles using hybrid fault-tolerant approach

Signal delay, execution deviation and reference model mismatch are common faults in autonomous vehicles, resulting in reduced path tracking control performance. To improve the path tracking performance of autonomous vehicles under fault conditions, this paper proposes a hybrid fault-tolerant (HFT) path tracking control approach, in which passive fault-tolerant path tracking control and fault observer-based hybrid active fault-tolerant path tracking control are introduced. The probable usual errors in path tracking are first examined and analyzed, together with the vehicle kinematic model and a typical autonomous driving system. Then, a slide-mode-based passive fault-tolerant path tracking controller is designed and integrated with a fault observer to create a hybrid active fault-tolerant controller. Finally, simulations and experiments are conducted, and the results demonstrate that our proposal is effective. After convergence of the fault observations, the lateral tracking error is less than 0.2m and the root mean square of the tracking error is 44% smaller than the LQR approach.
A Robust Sequential Recommendation Model Based on Multiple Feedback Behavior Denoising and Trusted Neighbors

Abstract At present, most of the personalized sequential recommendations utilize users’ implicit positive feedback (such as clicks) to predict user behavior, ignoring the impact of implicit negative feedback and explicit feedback on the accuracy of recommendation results prediction. In this paper, we propose a robust sequence recommendation model based on multi feedback behavior denoising and trusted neighbors, which utilizes multiple feedback behavior data for feature denoising and considers trusted nearest neighbor information to improve model performance. Firstly, by learning the feature representations and interactions of various types of feedback, explicit feedback is used to map and purify implicit feedback with the same and different attributes, resulting in unbiased user performance. Then, we design a filter attention network to identify highly trusted neighbor information. Finally, we integrate pure user interest representations and trusted nearest neighbor representations to improve the accuracy and robustness of the model. The experimental results on two publicly available datasets show that the proposed sequential recommendation model can achieve superior results to baseline methods in both AUC and RelaImpr.
State-of-the-Art Review of the Corpus Linguistics Field From the Beginning Until the Development of ChatGPT

The present paper highlights the recent state of and development in the corpus linguistics (CL) field. Although several reviews have been conducted on CL, these reviews have focused on specific areas, such as education, or did not provide an overall clear overview of the future implications of the field (Baker et al., 2008; Biber &amp; Reppen, 2020; Biber et al., 1998; G. N. Leech, 1991; Mcenery et al., 2019; McEnery &amp; Hardie, 2012). The author begins this paper with providing an overview that can guide new researchers in this field as well as postgraduates who require a general historical and thematic map of CL. The general overview discusses the publications of scholars who have participated in this field as well as the central tools that have been applied in CL. For specific details regarding the development of the field, the author analysed 217 articles from the 3 highest-impact factor journals according to the Web of Science over the last four years (2019–2022). The findings reveal a rapid development of the field in terms of practical and methodological perspectives, specifically regarding the investigations of language uses in different contexts. Thus, this paper indicates a significantly strong correlation between CL and technological development, such as natural language processing (NLP), and how this approach could fill the research gap of utilising CL in other areas of linguistics.
Student Performance in Machine Translation Training: Assessment and Exploration

The present research aims to conduct a process-oriented analysis to measure whether a group of graduate students enrolled in a translation course made steady progress in their performance of identifying machine translation (MT) errors and post-editing MT drafts of company web texts and news texts. A mixed methods approach consisting of quantitative and qualitative analyses was used. The findings show that there was a steady decline in the average number of MT errors that students could not spot or correctly identify in their three assignments. However, there was no significant improvement in student MTPE performance, with only a slight decrease in errors in the final MTPE assignment, which still remained worse than the first one. Finally, student responses in their reflection essays indicated that their reception of MT and MTPE had shifted from negative denial to positive acceptance. Overall, the findings of the present study reveal the need to extend the period of MTPE training for students. Incorporating MT training into the translation course has proven to be worthwhile for students, as it helps to dispel students’ previous misconception about MT and MTPE.
Penggunaan Media Video &amp; Gambar Dalam Pembelajaraan IPAS Pada Siswa Kelas 4 SDN 1 Wonoboyo

This research aims to explore the use of video and image media in learning Natural Sciences (IPAs) for grade 4 students at SDN 1 Wonoboyo. Visual media has become an important component in modern education, and this research investigates how the use of this media can influence students' understanding and motivation in learning sciences.&#x0D; The research results show that the use of video and image media in science learning positively influences students' understanding of the concepts being taught. Students also show higher interest and motivation to learn when visual media is used. Even though it requires more intensive preparation for teachers, the use of visual media is considered effective in explaining complex science concepts.&#x0D; This research provides insight into the importance of using visual media in improving the quality of science learning at the elementary level and suggests that teachers and schools continue to develop their skills in integrating this media in the curriculum. Video and image media have great potential in motivating students and facilitating better understanding of science material, so they can be effective tools in improving education at the elementary level.&#x0D;
Advancements in Machine Learning: A Comprehensive Exploration of Methods, Applications, and Future Perspectives

Machine learning, a specialized subset of artificial intelligence, imparts the ability to machines to learn, while artificial intelligence (AI) encompasses the broader field dedicated to emulating human capabilities. Within AI, machine learning employs computational techniques to instruct computers on learning from their historical experiences. Unlike models based on predetermined equations, machine learning algorithms derive insights directly from data, progressively improving their performance as the volume of learning examples grows. This paper presents a comprehensive overview of the domain, exploring diverse machine learning methodologies such as supervised, unsupervised, and reinforcement learning, along with an examination of various programming languages employed in machine learning applications. Keywords: Machine learning, Artificial intelligence, Computational techniques, Historical experiences, Learning examples, Supervised learning, Unsupervised learning, Reinforcement learning, Programming languages, Machine learning applications
The IBdata Web System for Biological Collections: Design Focused on Usability

The software design process must put users at the core of the process to enable them to meet their specific objectives effectively, efficiently, and successfully. Thus, a software design for a computing system to consult biological collections guided by the concept of usability will result in an effective and efficient biodiversity informatics tool. Here, we introduce IBdata, a web system to consult biological collections, developed using a design approach based on the architecture of three layers: database, business rules, and user interface. The user interface design was guided by the concept of usability focused on four core concepts: simplicity, adaptability, guide the user through the journey, and feedback. The IBdata web system that we developed is composed of three modules (query, capture and editing, and administration), permitting it to query a database with about 1.7 million specimen records. Biodiversity data query systems must be effective and efficient and should meet the user’s expectations. Software design methodologies play a central role in achieving these goals, and, in this context, interface design techniques that put the user at the core of development are valuable, as in the development of the IBdata web system.
Machine learning for effective EHR management in blockchain-cloud integration

&lt;p&gt;Machine learning (ML) techniques have gained prominence in effectively managing Electronic Health Record (EHR) systems within the context of blockchain-cloud integration. This study presents a hybrid Machine Learning approach that combines logistic regression (LR) and random forest (RF) techniques for EHR management, leveraging the data stored in a blockchain-cloud integrated system. The tamper-resistant nature of blockchain ensures the authenticity and security of the stored patient information, serving as a reliable source for learning. The proposed LR+RF model is evaluated against other algorithms, considering various performance metrics. The analysis reveals that the LR+RF model achieves an impressive accuracy rate of 98.37%, indicating its efficacy in accurately classifying EHR data and facilitating effective management. Furthermore, the study compares the performance of blockchain-cloud-based decentralized storage with blockchain-based storage and peer-to-peer storage in terms of latency and throughput. The results demonstrate that the blockchain-cloud integrated decentralized storage surpasses other storage methods, achieving an average throughput of 6.8 units and a latency of 4.7 units. These findings highlight the potential of the proposed LR+RF model for EHR management within a blockchain-cloud integrated environment. The use of blockchain as a secure storage environment ensures the integrity of patient information, while Machine Learning techniques enhance the accuracy of classification.&lt;/p&gt;
An Analysis of Students Reading Comprehension on Students Reading Aloud Activities at the XI Grade of MAN 1 Solok Selatan

Students' ability in reading, especially reading comprehension is still low. The first problem most of students have limited vocabulary in English, The second problem is that the teacher still uses the conventional strategies in class in reading comprehension. The third problem is students are not able to understand the words conveyed by the teacher orally in English. The purpose of this research was to find out student’s reading comprehension on student’s reading aloud activities at the XI Grade of MAN 1 Solok Selatan. Then, the researcher used descriptive quantitative method. In the collecting the data, test wich consisted of reading comprehension through students reading aloud activities. The data was analyzed by using T- Test Formula. Based on results shows that that 3 students get the score between 95 - 100. That 2 students get the score between 83 - 94. Then 13 students get score between 71 – 82. Then 5 students get the score between 59 – 70. Then 2 students get the score 47 – 58 and 7 students get the score between 35 – 46. In conclusion, the students reading comprehension on reading aloud activities at the XI.IPS.1 students at MAN 1 Solok Selatan was considered medium. Evidenced from when students reading aloud activities the students still confused, and not confidence.
The Effect of Product Knowledge and Market Orientation on Business Performance in MSMEs

Globalization has had a significant impact on the business world, especially in terms of expanding market reach and increasing competition. In the growing digital era, information and communication technology has affected many aspects of human life, including the business sector. The development of digital technology has accelerated the pace of business and enabled access to global markets on a larger and more efficient scale. This study aims to analyze the effect of product knowledge and market orientation on firm business performance mediated by product innovation and omnichannel on MSMEs in Boyolali district. There were 100 respondents in this study through an online survey using google form with a Likert scale. The sampling technique uses non probability sampling using the slovin method. Data analysis using SmartPLS. Validity and reliability tests using smart PLS analysis on the outer model. Hypothesis testing using the SmartPLS inner model. The analysis results show that Product Innovation is able to mediate the relationship between Market Oriented and Firm Business Performance and Omnichannel is able to mediate the relationship between Market Oriented and Firm Business Performance.
Target Signal Communication Detection of Black Flying UAVs Based on Deep Learning Algorithm

Background: Unmanned aerial vehicles (UAVs) are being widely used in many fields, such as national economy, social development, national defense, and security. Currently, the number of registered UAVs in China is far less than that of flying UAVs-the frequent occurrence of unsafe incidents. Objective: The phenomenon of UAVs flying undeclared and unapproved has caused more serious troubles to social public order and people's production and life. Methods: In this paper, to assist the public security department in detecting the phenomenon of UAV black flying, our team conducts a series of research based on the deep learning YOLOv5 (You Only Look Once) algorithm. Results: Firstly, the Vision Transformer mechanism is integrated to enhance the robustness of the model. Secondly, depth-separable convolution is introduced to reduce parameter redundancy. Finally, the SimAM attention-free mechanism and CBAM attention-free mechanism are combined to enhance the attention of small target UAVs. Conclusion: Through the analysis of UAV targets in video surveillance, the rapid identification of black-flying UAVs can be realized, the monitoring and early warning ability of UAVs in a specific area can be improved, and the loss of life and property of people can be reduced or saved as much as possible.