Reviewerly/exHarmony huggingface dataset:
Repairing $$\mathcal{{EL}}$$ Ontologies Using Weakening and Completing

The quality of ontologies in terms of their correctness and completeness is crucial for developing high-quality ontology-based applications. Traditional debugging techniques repair ontologies by removing unwanted axioms, but may thereby remove consequences that are correct in the domain of the ontology. In this paper we propose an interactive approach to mitigate this for $$\mathcal{{EL}}$$ ontologies by axiom weakening and completing. We present the first approach for repairing that takes into account removing, weakening and completing. We show different combination strategies, discuss the influence on the final ontologies and show experimental results. We show that previous work has only considered special cases and that there is a trade-off, and how to deal with it, involving the amount of validation work for a domain expert and the quality of the ontology in terms of correctness and completeness. We also present new algorithms for weakening and completing.
End-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning

As an indispensable module of computer-aided pronunciation training (CAPT) systems, mispronunciation detection and diagnosis (MDD) techniques have attracted a lot of attention from academia and industry over the past decade. To train robust MDD models, this technique requires massive human-annotated speech recordings which are usually expensive and even hard to acquire. In this study, we propose to use transfer learning to tackle the problem of data scarcity from two aspects. First, from audio modality, we explore the use of the pretrained model wav2vec2.0 for MDD tasks by learning robust general acoustic representation. Second, from text modality, we explore transferring prior texts into MDD by learning associations between acoustic and textual modalities. We propose textual modulation gates that assign more importance to the relevant text information while suppressing irrelevant text information. Moreover, given the transcriptions, we propose an extra contrastive loss to reduce the difference of learning objectives between the phoneme recognition and MDD tasks. Conducting experiments on the L2-Arctic dataset showed that our wav2vec2.0 based models outperformed the conventional methods. The proposed textual modulation gate and contrastive loss further improved the F1-score by more than 2.88% and our best model achieved an F1-score of 61.75%.
Obstacle Avoidance Based on Stereo Vision Navigation System for Omni-directional Robot

This paper addresses the problem of obstacle avoidance in mobile robot navigation systems. The navigation system is considered very important because the robot must be able to be controlled from its initial position to its destination without experiencing a collision. The robot must be able to avoid obstacles and arrive at its destination. Several previous studies have focused more on predetermined stationary obstacles. This has resulted in research results being difficult to apply in real environmental conditions, whereas in real conditions, obstacles can be stationary or moving caused by changes in the walking environment. The objective of this study is to address the robot’s navigation behaviors to avoid obstacles. In dealing with complex problems as previously described, a control system is designed using Neuro-Fuzzy so that the robot can avoid obstacles when the robot moves toward the destination. This paper uses ANFIS for obstacle avoidance control. The learning model used is offline learning. Mapping the input and output data is used in the initial step. Then the data is trained to produce a very small error. To support the movement of the robot so that it is more flexible and smoother in avoiding obstacles and can identify objects in real-time, a three wheels omnidirectional robot is used equipped with a stereo vision sensor. The contribution is to advance state of the art in obstacle avoidance for robot navigation systems by exploiting ANFIS with target-and-obstacles detection based on stereo vision sensors. This study tested the proposed control method by using 15 experiments with different obstacle setup positions. These scenarios were chosen to test the ability to avoid moving obstacles that may come from the front, the right, or the left of the robot. The robot moved to the left or right of the obstacles depending on the given Vy speed. After several tests with different obstacle positions, the robot managed to avoid the obstacle when the obstacle distance ranged from 173 – 150 cm with an average speed of Vy 274 mm/s. In the process of avoiding obstacles, the robot still calculates the direction in which the robot is facing the target until the target angle is 0.
Real-Time Occluded Face Identification Using Deep Learning

One of the most difficult aspects of face identification is face occlusion. Face occlusion is when anything is placed over the face, for example, a mask. Masks occlude multiple important facial features, like the chin, lips, nose, and facial edges. Face identification becomes challenging when important facial features are occluded. Using one of the deep learning algorithms, YOLOv5, this work tries to identify the face of someone whose face is occluded by a mask in real-time. A special program is being created to test the effectiveness of the YOLOv5 algorithm. 14 people's data were registered, and each person had 150 images used for training, validation, and testing. The images used are regular faces and mask-occluded faces. Nine distinct configurations of epoch and batch sizes were used to train the model. Then, during the testing phase, the best-performing configuration was chosen. Images and real-time input were used for testing. The highest possible accuracy of image identification is 100%, whereas the maximum accuracy of real-time identification is 64%. It was found during the testing that the brightness of the room has an influence on the performance of YOLOv5. Identifying individuals becomes more challenging when there are significant changes in brightness.
Connecting Chatman to This Moment

In this editors' note, guest editors Amelia N. Gibson and Nicole A. Cooke introduce the special issue, "Chatman Revisited: Re-examining and Resituating Social Theories of Identity, Access, and Marginalization in LIS."
A cGAN-based tumor segmentation method for breast ultrasound images

Abstract Objective . This paper proposes a conditional GAN (cGAN)-based method to perform data enhancement of ultrasound images and segmentation of tumors in breast ultrasound images, which improves the reality of the enhenced breast ultrasound image and obtains a more accurate segmentation result. Approach . We use the idea of generative adversarial training to accomplish the following two tasks: (1) in this paper, we use generative adversarial networks to generate a batch of samples with labels from the perspective of label-generated images to expand the dataset from a data enhancement perspective. (2) In this paper, we use adversarial training instead of postprocessing steps such as conditional random fields to enhance higher-level spatial consistency. In addition, this work proposes a new network, EfficientUNet, based on U-Net, which combines ResNet18, an attention mechanism and a deep supervision technique. This segmentation model uses the residual network as an encoder to retain the lost information in the original encoder and can avoid the gradient disappearance problem to improve the feature extraction ability of the model, and it also uses deep supervision techniques to speed up the convergence of the model. The channel-by-channel weighting module of SENet is then used to enable the model to capture the tumor boundary more accurately. Main results . The paper concludes with experiments to verify the validity of these efforts by comparing them with mainstream methods on Dataset B. The Dice score and IoU score reaches 0.8856 and 0.8111, respectively. Significance . This study successfully combines cGAN and optimized EfficientUNet for the segmentation of breast tumor ultrasound images. The conditional generative adversarial network has a good performance in data enhancement, and the optimized EfficientUNet makes the segmentation more accurate.
A Study of Prediction of Airline Stock Price through Oil Price with Long Short-Term Memory Model

This study aims to present a model that predicts the stock price of an airline by setting the economic and technical information of oil as features and taking advantage of the LSTM method.In this study, oil price data for about seven years from January 4, 2016, to April 14, 2023, were collected through FinanceDataReader.The collected data is a total of 1,833 days of AA stock price data.The price data consists of six categories: Date, Open, High, Low, Close, Volume, and Change (price is based on dollars).Data is stored every 24 hours, so it was judged to be most suitable for short-term price prediction (24 hours later) to be conducted in this study.In this paper, normalized closing price data was trained for 50 epochs.As a result of learning, the loss value converged close to 0. The MSE measured by the accuracy of the model shows a result of 0.00049.The significance of this study is as follows.First, it is meaningful in that it can present indicators such as more sophisticated predictions and risk management to airline companies.Oil price as our selected feature can compensate for the poor performance of a simple model and its limitations on overfitting.
Development of Theme - Based English Learning Model with Contextual Teaching and Learning (CTL) Approach to Improve Students’ Reading Skills

This study is a Research and Development (R &amp; D) which aims to (1) describe the needs of Theme-Based English Learning Model Development with CTL Approach to improve students' reading skills, (2) design a prototype of theme-based English learning model development with CTL approach to improve students' reading skills, (3) to measure the validity level of the development of theme-based English learning model with CTL approach to improving students' reading skills, (4) to measure the practicality level of the development of theme-based English learning model with CTL approach to improving students' reading skills, and (5) to measure the effectiveness level of the development of theme-based English learning model with CTL approach to improving students' reading skills. Subject of development of theme-based English learning model with contextual teaching and learning (CTL) approach to improve students' reading skills at SMP Negeri 19 Makassar. The research used a type of research and development (R&amp;D) research that refers to the ADDIE model, Sugiyono (2016: 298). This model consists of five stages, namely Analysis, Design, Development, Implementation and Evaluation. Data collection techniques were carried out using observation, interviews, tests, questionnaires, and documentation techniques. The research instruments used were learning implementation observation instruments, student learning outcomes assessment test instruments, learning device validation instruments and implementation observation sheet validation instruments. The data analysis techniques were carried out by (1) analysing needs, (2) analysing the level of validity, (3) analysing the level of effectiveness, and (4) analysing student and teacher response data. The results of this study indicate that there is a need to develop a theme-based English learning model with a CTL approach to improve students' reading comprehension skills. The validation test results showed that the model book, teaching manual, learning tools and several research questionnaires were declared very valid based on the experts' assessment. Furthermore, the results of the practicality test also stated that this model proved to be very practical based on the results of observations of learning implementation and teacher and student response questionnaires.
IMPLEMENTASI SISTEM INFORMASI NILAI MATA PELAJARAN BERBASIS WEB DENGAN FITUR PEMILIHAN SISWA BERPRESTASI AKADEMIK MENGGUNAKAN METODE PROFILE MATCHING

The use of a computerized system saves data processing time, data storage can be safer than manual storage and can reduce the human error factor. There fore the use of information systems as a data management tool must be designed according to existing needs. This is what is needed by SMP N 5 Palu because the management of existing subject values is still done manually so it has weaknesses, including recording grades, making attendance reports, and making report cards which is quite difficult to collect lesson values from each teacher so that the recording process becomes hampered and The process of selecting outstanding students which has been carried out so far by the school still has several weaknesses as a result it raises several problems, including the processing of data for selecting outstanding students which takes a long time and the possibility of human error occurring in data processing. Therefore, a web-based subject score information system was created with the feature of selecting outstanding students using the Profile Matching method. This system was developed using the Profile Matching method, which is a multi-criteria decision-making method that has very simple calculations but has good level of selectivity in determining an alternative. The results of this study are in the form of applications that can help provide information on subject values and decisions in determining students' academic achievements at the school.
Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52.
Incremental Verification of Neural Networks

Complete verification of deep neural networks (DNNs) can exactly determine whether the DNN satisfies a desired trustworthy property (e.g., robustness, fairness) on an infinite set of inputs or not. Despite the tremendous progress to improve the scalability of complete verifiers over the years on individual DNNs, they are inherently inefficient when a deployed DNN is updated to improve its inference speed or accuracy. The inefficiency is because the expensive verifier needs to be run from scratch on the updated DNN. To improve efficiency, we propose a new, general framework for incremental and complete DNN verification based on the design of novel theory, data structure, and algorithms. Our contributions implemented in a tool named IVAN yield an overall geometric mean speedup of 2.4x for verifying challenging MNIST and CIFAR10 classifiers and a geometric mean speedup of 3.8x for the ACAS-XU classifiers over the state-of-the-art baselines.
Investment Decision on Cryptocurrency: Comparing Prediction Performance Using ARIMA and LSTM

The increasing popularity of cryptocurrencies as a means of financial inclusion for investment and trade has become a major concern for individuals seeking to benefit from the cryptocurrency market. This study aims to provide insights for cryptocurrency investors, financial sector professionals, and academics by utilizing machine learning techniques such as ARIMA and LSTM to compare the accuracy of modeling performance on datasets predicting the prices of five cryptocurrencies, namely Bitcoin, Ethereum, Binance Coin, Tether, and Cardano. Data was obtained by downloading from the Yahoo Finance website using Jupyter notebook. The LSTM method outperformed the ARIMA method, achieving a lower MAPE value of less than 10 percent and effectively capturing price movements, providing valuable information for decision-making.
Design &amp; Development of Automatic Vehicle Mover Using LIDAR System

The under-development systems will make it possible for previously unachievable technology to function in autonomous or self-driving automobiles.Improvements are done in the areas of vehicle safety and speed in accordance with our work, saving us time.The majority of accidents that occur on our roads are caused by drivers who are unable to react quickly enough to sudden circumstances.The rotating LIDAR sensor atop an autonomous vehicle plays a crucial role in the vehicle's ability to navigate its environment.Atop the car, this sensor gathers information about its immediate surroundings, which the navigation system uses to steer the vehicle safely in the desired route.When compared to conventional driving methods, this one has a lot more potential uses.Automated systems that monitor traffic in real time and respond to abnormalities are one solution.The time and effort spent on daily activities can be reduced significantly thanks to this technique.When this system is established or developed for self-driving automated cars, it will make the monitoring and identification of moving objects, which have been the most difficult tasks for decades, much simpler.
MANAGING THE EDUCATIONAL PROCESS USING WEB 2.0 SERVICES (USING THE EXAMPLE OF LYCEUM №1 «SPUTNIK»)

In the context of the development of human civilization, digital technologies are increasingly entering our lives. Education as a social institution largely determines the ways of development of society. In this regard, educational organizations need to constantly improve the communication system for more effective functioning and development, as well as use modern means of communication. The introduction of information technologies into the management process significantly increases the efficiency of communication processes in the organization and contributes to the effective solution of management tasks. In this article, on the basis of a single educational institution, the possibilities of using Web 2.0 services in the implementation of the management of the educational process are considered. The authors come to the conclusion that Web 2.0 services and its constituent information and communication educational platform "Sferum", cloud storage "Yandex.Disk", services for teamwork with documents "Yandex.Documents" and online surveys "Yandex.Forms”, a service for creating and editing Mindomo mind maps can be used as one of the ways to digitalize the management process in an educational organization. To determine the degree of need for the proposed resources, work was carried out with the teaching staff (questionnaires, observation, conversations, testing).
Improving Inertial Sensor-based Human Activity Recognition using Ensemble Deep Learning

Sensor-based human activity recognition (S-HAR) is a famous study focusing on detecting human physiological actions by interpreting various sensors, especially one-dimensional time series information. Typically, S-HAR machine learning methods were developed using handcrafted characteristics. Unfortunately, this is a complicated process that involves feature engineering and a high level of domain knowledge. Due to the development of deep neural networks, classification techniques could efficiently handle relevant characteristics from raw sensor data, resulting in enhanced classification outcomes. In this study, we describe a unique method for S-HAR based on ensemble deep learning with sensor nodes connected to the waist, chest, leg, and arm. Implementing and training three deep learning networks is performed using a publically available dataset, including wearable sensors from eight human actions. The findings demonstrate that the proposed Ens-ResNeXt model provides the maximum accuracy and F1-score, which is superior to existing techniques.
RockNet: Rockfall and Earthquake Detection and Association via Multitask Learning and Transfer Learning

Seismological data plays a crucial role in timely slope failure hazard assessments. However, identifying rockfall waveforms from seismic data poses challenges due to their high variability across different events and stations. To address this, we propose RockNet, a deep-learning-based multitask model capable of detecting both rockfall and earthquake events at both the single-station and local seismic network levels. RockNet consists of two submodels: the single-station model, which computes waveform masks for earthquake and rockfall signals and performs earthquake <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$S$ </tex-math></inline-formula> phase picking simultaneously on single-station seismograms, and the association model, which determines the occurrences of local seismic events by aggregating hidden feature maps from the trained single-station model across all stations. Since the rockfall data is relatively scarce and may not be sufficient to train a deep-learning model effectively, we augment the dataset with abundant nonrockfall data and add additional tasks to promote shared interpretability and robustness. RockNet is trained and tested on a local dataset collected from the Luhu tribe in Miaoli, Taiwan, achieving macro F1-scores of 0.983 and 0.990 for the single-station model and the association model, respectively. Furthermore, we evaluate RockNet on an independent dataset collected from the Super-Sauze unstable slope region in France, and it demonstrates good generalization performance in discriminating earthquake, rockfall, and noise with a macro F1-score of 0.927. This study highlights the potential of deep learning in leveraging diverse types of inputs for seismic signal detection even with limited training data.
Substantial security challenge to web applications, using modified OTC and OWASP update

Internet security is studied by computer science and serves as a safe medium for exchanging data while minimizing the likelihood of online threats.Through use of advanced web-based software is growing because they provide the user with a lot of features.Web technologies have an important role in different industries, like schooling, retail, medical care, and payment systems.Session bugs are becoming more common throughout web applications whereas their value in community grows.Hackers try to profit of incorrectly designed websites so they take hold of victim's sessions and also of identities.As a result, session handling represents a substantial security challenge to web applications.Weak programming methods are among the causes for effective session acquisition.A further explanation is that the server as well as the customer verify themselves differently at first.In the recent years most common strikes used amongst attackers is session hijacking.Based on latest recent OWASP update, session hijacking is indeed one of the second frequent assault that happens mostly.It is one important attack among others, which a hacker may use to connect directly to a customer's operating session.User Hijacking occurs when a hacker takes victim's session id, and uses it to obtain entry into the victim's actual session.This system will provide protection in case of this attack form once it has been successfully implemented.