Reviewerly/exHarmony huggingface dataset:
Transfer Learning Method for Object Detection Model Using Genetic Algorithm

This paper proposes a transfer learning method for an object detection model using a genetic algorithm to solve the difficulty of the conventional transfer learning of deep learning-based object detection models. The genetic algorithm of the proposed method can select the re-learning layers automatically in the transfer learning process instead of a trial-and-error selection of the conventional method. Transfer learning was performed using the EfficientDet-d0 model pre-trained on the COCO dataset and the Global Wheat Head Detection (GWHD) dataset, and experiments were conducted to compare fine-tuning and the proposed method. Using the training data and the validation data of the GWHD, we compare the mean average precision (mAP) of the models trained by the conventional and the proposed methods, respectively, on the test data of the GWHD. It is confirmed that the model trained by the proposed method has higher performance than the model trained by the conventional method. The average of mAP of the proposed method, which automatically selects the re-learning layer (≈0.603), is higher than the average of mAP of the conventional method (≈0.594). Furthermore, the standard deviation of results obtained by the proposed method is smaller than that of the conventional method, and it shows the stable learning process of the proposed method.
Evaluation of students’ awareness and usage of digital learning sources in academic libraries: College of Business- King Khalid University, Saudi Arabia

Information and communication technology (ICT) has become one of the most important components for improving the performance of educational institutions and provide various learning sources in academia. Unlike conventional hard copy sources of information, electronic (e-) sources use modern technologies to store, process and disseminate information effectively. However, there is still a lack of awareness about the importance of digitisation and informatics in the academic community, especially in the Arab world. Therefore, this study explores the importance of ICT and its role in providing e-sources to improve the educational system at King Khalid University (KKU). Moreover, information awareness is evaluated among the students of the College of Business at KKU. The issues of e-sources and their impact on the use of ICT to access data are further investigated. Finally, the factors that limit access to e-sources at KKU are highlighted.
Ten Years Practice of Manufacturing Education with Social Contributions Through Development and Donation of Teaching Materials Used in Schools for the Visually Impaired

Ten years have passed since the missions to develop and donate teaching materials for whole Japanese school for the visually impaired started in our university. Through newly developed teaching materials, mutual educational support relationship has been established between the university and the schools for the visually impaired. Evaluation of those activities as engineering design training, and educational outputs were explained.
Accuracy-efficiency Balanced Sampling Rate Found by Discrete Fourier Transform

Though Vision-based human action recognition has received extensive research, there are few works to explore the sampling rate of videos, which is essential in practical application. Too high sampling rates result in waste of computing resources and reduced efficiency, while too low sampling rates cause a rapid drop in accuracy. In this paper, we propose a method based on Discrete Fourier Transform (DFT) to find out a sampling rate which can balance efficiency and accuracy. This method captures the variation of video features in the frequency domain, which helps to choose a sampling rate based on how much information is required. We propose a 3D convolutional neural network for arbitrary input length (AC3D) and conduct extensive experiments on two widely used human action recognition datasets: UCFIOI and HMDB51. Experimental results show that DFT-based method can find a sampling rate with a good balance of efficiency and accuracy.
Design of Interactive Guidance Platform and Data Fusion Analysis of English Courses in Mobile JavaScript Environment

Under the ubiquitous learning environment, design an English information sharing platform based on JavaScript technology. By giving the overall English information sharing platform structure, each hardware part is analyzed in detail, and RSS technology is used to improve the rate of English information push and integration, and improve English information Sharing speed, the method of integrating information technology and college English teaching in the Internet era, in order to provide feasible ideas for college English informatization teaching, and finally based on JS to study the design of the English course mutual assistance guidance platform and the data fusion model.
Construction of Data Intelligent Evaluation System for Children's Sense of Humor based on the Fusion of Facial Expression Recognition and Blink Frequency Recognition Algorithms

Based on the relationship analysis of the subjects' left forehead EEG signal fusion expression recognition and blink frequency recognition algorithms and Blink data, the optimal window width and classification algorithm were screened respectively, and a humor detection algorithm suitable for wearable devices was designed. , and then construct a data intelligent evaluation system of children's sense of humor through Bayesian network based on student information and contextual information of teaching activities. The sense of humor in the kindergarten is mainly for viewing; the sense of humor focuses on education; the safety guarantee pays attention to the feedback of space, and the evaluation form of sports space in kindergarten is obtained according to the system.
Uso de Meta-Learning em Tarefas de Aprendizado Profundo

This book of the XXXVII Brazilian Symposium on Databases (SBBD 2022) includes three book chapters written by the authors of the selected short courses and presented in the edition of the event held from September 19 to 23, 2022. The chapters address issues related to Natural Language Processing, NoSQL Database Design, and Using Meta-Learning in Deep Learning tasks. The short course program committee was composed of professors Ticiana L. Coelho da Silva (UFC), Damires Yluska de Souza Fernandes (IFPB), and Anne Magály de Paula Canuto (UFRN), under the coordination of the former. The richness of this issue can be mainly credited to the authors and reviewers. We thank them a lot for their insightful contributions and discussions during SBBD 2022.
Exploring the relationship between digital literacy skills and Technological Pedagogical and Content Knowledge (TPACK) among secondary school teachers

There is an increasing demand for teachers to present technological pedagogical and content knowledge (TPACK) in their classrooms with the help of their digital literacy skills (DLS). Though the teachers at the secondary school level are provided with digital gadgets, however, the teachers are still struggling with the use of digital tools during the teaching-learning process. Lack of digital literacy might be the cause of this reluctance, so the current study aims to explore the relationship between the digital literacy skills of teachers with respect to Technological, Pedagogical and Content Knowledge (TPACK).Data was collected from four hundred school teachers through Digital Literacy Scale (DSL) and TPACK inventory. The results indicate a low level of digital literacy and knowledge of TPACK, identified through descriptive statistics.However, a significantly positive relationship was identified through a significant correlation relationship ((r =.587, p equal or less then .001) between both variables.On the basis of results, more trainings are recommended for teachers on both digital literacy and content pedagogy.
Biometric presentation attacks: Handcrafted features versus deep learning approaches

Attribute Filters are powerful image simplification operators that have very good contour-preservation properties. They can be efficiently computed using morphological trees. The most common strategy to compute attribute filters using morphological trees is based on three steps: (i) tree construction: the tree is built along with its attributes; (ii) filtering: we simplify the tree by removing some of its nodes based on some attribute and a filtering rule defined on a threshold value; and (iii) image reconstruction: this is the final phase where the tree is converted back to an image leading to an attribute filter. However, after filtering a few attributes in tree may change (we call them as non-flat attributes) and they must be updated, when other attribute filters are applied to the simplified tree again, with either a different or the same threshold value. In this paper we present an efficient algorithm to update non-flat and incremental attributes in morphological trees with low memory consumption and fast computation.
ITS Traffic Violation Regulation Based on Blockchain Smart Contracts

In the intelligent transportation system, many challenges have been identified to achieve a fully functional, practical and integrable ITS network. Some of these challenges include the regulation of traffic violations. Therefore, a proposed blockchain smart contract-based method has been formulated to address this challenge. This method includes vehicle speed detection, vehicle information collection through vehicle re-identification technique and a blockchain part containing smart contracts. The smart contract is a conditioned filter that follows regulatory rules from reporting violation points and fines for each violation to penalties. We have implemented the proposed algorithm and presented an evaluation of the proposed method, which gives satisfactory results and finally compare it with other known methods.
OpenCitations: an Open e-Infrastructure to Foster Maximum Reuse of Citation Data

OpenCitations is an independent not-for-profit infrastructure organization for open scholarship dedicated to the publication of open bibliographic and citation data by the use of Semantic Web (Linked Data) technologies. OpenCitations collaborates with projects that are part of the Open Science ecosystem and complies with the UNESCO founding principles of Open Science, the I4OC recommendations, and the FAIR data principles that data should be Findable, Accessible, Interoperable and Reusable. Since its data satisfies all the Reuse guidelines provided by FAIR in terms of richness, provenance, usage licenses and domain-relevant community standards, OpenCitations provides an example of a successful open e-infrastructure in which the reusability of data is integral to its mission.
Code Smell Co-occurrences: A Systematic Mapping

Code smells co-occurrences, i.e., occurrences of more than one code smell in the same class or method, can be better indicators of design problems for software quality. Despite its importance as an indicator of design problems, we have little known about the impact of removing the smells co-occurrence via software refactoring on internal quality attributes, such as coupling, cohesion, complexity, and inheritance. There are several literature reviews on code smells and refactoring. However, we did not identify any literature review investigating the impact of co-occurring code smells in primary studies. Thus, this work presents a systematic mapping of code smell co-occurrences state of the art. To guide the mapping, we defined the following research questions: (i) Which code smells are most tend to co-occur?; (ii) How are code smells co-occurrences refactored?; (iii) What is the impact of code smell co-occurrences on internal quality attributes?; (iv) Which datasets are considered to analyze code smells co-occurrences?; (v) How are code smells co-occurrences detected?; and, (vi) What are the most common code smells co-occurrences?. With main findings, we identified that the code smells that most co-occur are: Feature Envy (19 co-occurrences), Long Method (14 co-occurrences), Long Parameter List (11 co-occurrences) and God Class (11 co-occurrences). No studies were found to answer how the code smells co-occurrences are refactored and the co-occurrences that most impacted the quality attributes, all of which were negative. Only three datasets were considered in the studies to analyze the code smell co-occurrences. Manual detection of co-occurrences is the most used. The co-occurrences Class Data Should Be Private + Feature Envy and Feature Envy + Long Method were the most common co-occurrences in the studies.
Explore Adversarial Attack via Black Box Variational Inference

From the perspective of probability, we propose a new method for black-box adversarial attack via black-box variational inference (BBVI), where the knowledge of victim model is unavailable. Instead of obtaining a single point, the proposed method focuses on approximating the probability distribution of adversarial examples. Thus, infinite adversarial examples can be drawn from the inferred distribution. Although the Monte Carlo estimator in BBVI is unbiased, its variance brings unstable gradient estimation, which leads to poor attack performance and low query efficiency. To reduce variance, we improve the BBVI with importance sampling which guided by a surrogate model to obtain a better estimator of gradient, which enhances both success rate and query efficiency. Extensive experiments on ImageNet dataset well demonstrate the outperformance of the proposed method compared with prior arts.
A case of productive confirmation framing in an introductory lab

Students' framing of an activity -their understanding of "what is it that's going on here" [1] -shapes how they act, think, and learn.Prior research suggests that framing instructional physics laboratory activities as confirming known results is problematic for learning [2,3].Here, we complicate those findings by presenting a case-study of students who exhibit confirmation framing as they engage in productive behavior.In this case, data that are inconsistent with the theoretical model of the lab motivate a genuine problem for the three students, who troubleshoot their apparatus and analyze their data to construct an explanation for this anomaly.We claim that their productive behavior is supported by their confirmation framing; put another way, we claim that their confirmation framing engenders their productive behavior: the students seek to explain how they could have caused this error.The case-study reported on here is part of a larger project studying student behavior in nontraditional physics labs.
An Interpretable Neural Network Model for Bundle Recommendations

A users’ preference for a bundle – a set of items that can be purchased together – can be expressed by the utility of this bundle to the user. The multi-attribute utility theory motivate us to characterize the utility of a bundle using its attributes to improve the personalized bundle recommendation systems. This extended abstract for the Doctoral Symposium describes my PhD project for studying the utility of a bundle using its attributes. The steps taken and some preliminary results are presented, with an outline of the future plans.
GEOLOGICAL FEATURES IDENTIFIED FROM FIELD OBSERVATIONS AND REMOTE SENSING DATA ON THE UM TAGHIR AREA, EASTERN DESERT, EGYPT

The current study presents the integration between field observations and remotely sensed data for detection and extraction of geological structural features using Sentinel-2A and Aster DEM images. The area under investigation is represented by the Neoproterozoic East African Orogeny encompassing a part of the Arabian Nubian Shield. All the rock units studied belong to the Late Cryogenian-Ediacaran periods that are divided into two large terrains (continental arc terrain and oceanic arc terrain). The metagabbro and metavolcaniclastic rocks make up the oceanic terrain, while the gabbro, tonalite, granodiorite, dokhan volcanic, monzogranite and alkali feldspar granite comprise continental arc terrain. The Sentinel-2A remote sensing and ASTER DEM data have meaningful application in respect of geological interpretation. Lineament analysis is one of the most useful tools in geological mapping and mineral exploration. Several methods of processing and extracting lithological information and lineaments were applied to the Sentinel-2A and ASTER DEM data covering the present study. The methods include various image enhancements (FCC, MNF and PCA) and the application of directional filters (Sobel). The study results show that the area was subjected to stresses of various directions (WNW – ESE, NW-SE, NE-SW, N-S, NNE – SSW, and E-W). There occurred some important structure-related and mineralization events like migmatization (in granodiorites) and bearing mineralization (in gabbro), which are associated with major elements of evidence-based structural control of the area and with the proximity of the Quena Safaga shear zone-related mineralization.
Improving the Reader’s Attention and Focus through an AI-Driven Interactive and User-Aware Virtual Assistant for Handheld Devices

This paper describes the design and development of an AI-driven, interactive and user-aware virtual assistant aimed at helping users to focus their attention on reading or attending to other long-lasting visual tasks. The proposed approach uses computer vision and artificial intelligence to analyze the orientation of the head and the gaze of the user’s eyes to estimate the level of attention during the task, as well as administer effective and balanced stimuli to correct significant deviations. The stimuli are provided by a graphical character (i.e., the virtual assistant), which is able to emulate face expressions, generate spoken messages and produce deictic visual cues to better involve the user and establish an effective, natural and enjoyable experience. The described virtual assistant is based on a modular architecture that can be scaled to support a wide range of applications, from virtual and blended collaborative spaces to mobile devices. In particular, this paper focuses on an application designed to integrate seamlessly into tablets and e-book readers to provide its services in mobility and exactly when and where needed.
The effect of transformational leadership practices on staffs’ job satisfaction at Gezira University in Sudan

This study aimed to investigate the effect of transformational leadership on job satisfaction among staff of Gezira University. To this end, this study used a correlational research design. 332 participants were taken through stratified random sampling techniques. Data were collected from staff members using a closed-ended questionnaire. Data were analyzed through descriptive and inferential statistics like mean scores, one-sample t-test, Pearson correlation, and multiple regression analysis respectively. The study found that transformational leadership was practiced at Gezira University with its different dimensions. The aggregate level of staff job satisfaction at Gezira University was high in general. The results also indicate that there was a positive and moderate relationship between transformational leadership practices and job satisfaction at Gezira University. Besides, transformational leadership effect the level of job satisfaction at Gezira University. The study recommended Gezira University should organize training programs, courses, and workshops aimed at equipping staff members with leadership skills and competencies that enhance the practices of transformational leadership.
Sound in Time-Based Media Art

This chapter explores sound and its use in artworks and focuses on the fundamental science of sound and good practices for working with it both digitally and in a real space. It explores the structure of digital audio, sample rates, bit depths and containers, and issues specific to audio restoration and the production of exhibition files. Audio stored as a series of bytes representing the voltage changes of an analog audio signal is known as pulse-code modulated audio (PCM). The digital data stored on a CD or in an Audio Interchange File Format (AIFF) file or in a Waveform Audio File Format file all qualify as PCM audio. Apple created AIFF, IBM and Microsoft created the WAV, and Sony and Philips created the Audio CD standard. Passive speakers are connected to the amplifier using two wires, positive and negative: one carrying the signal and another for the ground.
Using AI to combat fraud in the voice channel

Whether it's an attempt to defraud individuals by accessing their bank accounts, or a large-scale attack on an organisation targeting huge volumes of data, identity is at the heart of most sophisticated criminal activity today. And while most online channels have been continually strengthened to stay ahead of evolving criminal tactics, the voice channel – another critical customer connection point – has increasingly come to be seen as a vulnerable target.
A COMPREHENSIVE STUDY ON INTERNET OF THINGS (IOT): COMPLETE JOURNEY TOWARDS A SUSTAINABLE AND SMART FUTURE

The Internet of Things (IoT) describes a kind of network that connects different devices exploiting the net.I create objects 'smart' by permitting them to transmit knowledge and automatize tasks while not in the absence of physical interference.The wearable health monitor is an Associate in Nursing example of a straightforward IoT in our life.
SASE and the future of IoT security

Secure Access Service Edge (SASE) is starting to become mainstream. And this approach promises unified policies that improve agility, fortify security and simplify the network stack – especially when it comes to the Internet of Things (IoT).
Pupil Masking and Rotated Sub-apertures

We optimised the shapes of the sub-apertures both in aperture masking and for segmented telescopes, by improving their MTF, and allowing for easier alignment. With ellipses or ovoids, our lab model also showed better convergence.
Online environmentally adaptive trajectory planning for rotorcraft unmanned aerial vehicles

Purpose This paper aims to propose an environmentally adaptive trajectory planning system considering the dynamic characteristics of unmanned aerial vehicles (UAVs) and the distance between obstacles and the UAV. The system generates a smooth and safe flight trajectory online. Design/methodology/approach First, the hybrid A* search method considering the dynamic characteristics of the quadrotor is used to find the collision-free initial trajectory. Then, environmentally adaptive velocity cost is designed for environment-adaptive trajectory optimization using environmental gradient data. The proposed method adaptively adjusts the autonomous flight speed of the UAV. Finally, the initial trajectory is applied to the multi-layered optimization framework to make it smooth and dynamically viable. Findings The feasibility of the designed system is validated by online flight experiments, which are in unknown, complex situations. Practical implications The proposed trajectory planning system is integrated into a vision-based quadrotor platform. It is easily implementable onboard and computationally efficient. Originality/value A hybrid A* path searching method is proposed to generate feasible motion primitives by dispersing the input space uniformly. The proposed method considers the control input of the UAV and the search time as the heuristic cost. Therefore, the proposed method can provide an initial path with the minimum flying time and energy loss that benefits trajectory optimization. The environmentally adaptive velocity cost is proposed to adaptively adjust the flight speed of the UAV using the distance between obstacles and the UAV. Furthermore, a multi-layered environmentally adaptive trajectory optimization framework is proposed to generate a smooth and safe trajectory.