Reviewerly/exHarmony huggingface dataset:
The Use of UML Diagrams to Enhance Dynamic Feature Location Techniques

Unified Modeling Language (UML) is an efficient technique that can be implemented to model and visualize an object-oriented system that suffers from lack or poor documentation. UML diagrams enhance the developers’ understanding the software system to apply software maintenance, software evaluation, or Dynamic Feature Location Techniques (DFLTs). The construction of UML diagrams is based on dynamic or static analysis. Dynamic analysis refers to collecting data when the system executes to generate execution traces, while static analysis refers to collecting the data from the source code. This paper implements UML diagrams in both directions, dynamic and static analysis, to enhance the software programmers applying DFLTs. A use case scenario diagram is used to specify the inputs of the DFLTs based on static analysis. In contrast, class diagrams and object diagrams are used to locate the required software artifacts of source code to construct the exercised feature based on dynamic analysis. Each created use case scenario generates an execution trace. The collected execution trace is analyzed to build class and object diagrams. The implemented case study illustrates the process of the proposed approach and shows that using UML diagrams increases the accuracy of applying DFLTs.
AI Based Actors Identification with High Intra-Class Variations

While deep learning based face recognition sur-passes human performance in constrained settings, it still strug-gles to achieve similar results applied in completely unconstrained settings. This paper explores the effectiveness of state-of-the-art face recognition models in the specific case of identifying actors in a historical photography collection of a Theatre Museum. Actors can be pictured at different angles and poses, at a different age, with masks and costumes leading to strong intra-class variations. In addition, images might show signs of decay due to their historical nature, further increasing the difficulty for a face recognition model to make correct predictions. This paper shows that ElasticFace, a face recognition model trained using a novel learning loss strategy, achieves 79.6% accuracy on the museum's photo database. Based on those outcomes, deploying face recognition to analyse historical image collections delivers valuable results for historians.
Arabic Sign Language Detection Using Deep Learning

Deaf or hard of hearing person number roughly 466 million people globally. Individuals in this situation must interact with people, gain new skills, and participate in activities. They use sign language to communicate with the rest of the world. The development of an autonomous system to detect gesture language has various difficulties, particularly for Arabic sign language. We built models for Arabic Sign Language to accomplish this. We used the Arabic Alphabets Sign Language (ArSL) Dataset, which contains 32 categories of sign characters, A total of 40 people of various ages participated in these Arabic sign and letter classes. There are 506 images in each category, for a total of 506 * 32 = 16192 images. 80 percent of the dataset was used for training, while 20 percent was used for testing. The results of the proposed system were 100 percent accurate.
Lightweight Neural Network-based Web Fingerprinting Model

Onion Routing is an encrypted communication system developed by the U.S. Naval Laboratory that uses existing Internet equipment to communicate anonymously. Miscreants use this means to conduct illegal transactions in the dark web, posing a security risk to citizens and the country. For this means of anonymous communication, website fingerprinting methods have been used in existing studies. These methods often have high overhead and need to run on devices with high performance, which makes the method inflexible. In this paper, we propose a lightweight method to address the high overhead problem that deep learning website fingerprinting methods generally have, so that the method can be applied on common devices while also ensuring accuracy to a certain extent. The proposed method refers to the structure of Inception net, divides the original larger convolutional kernels into smaller ones, and uses group convolution to reduce the website fingerprinting and computation to a certain extent without causing too much negative impact on the accuracy. The method was experimented on the data set collected by Rimmer et al. to ensure the effectiveness.
ESTIMATION OF NATURAL SCIENCES FACULTIES STUDENTS` SATIS-FACTION WITH ONLINE STUDYING AT THE UNIVERSITY

The satisfaction of students of the Engineering and Physics Faculty of High Technologies of USU with the quality of online learning during the pandemic was studied. The criteria and indicators of satisfaction with online learning are determined based on the implementation of expectations from online learning and the social and professional adaptation of students to online conditions. The diagnostic tools included a questionnaire to identify the implementation of students ' expectations from online learning and a methodology for its processing. The level of adaptation was measured by a seven-level scale of self-assessment of psychological comfort in online and offline classes. The results of the study showed a fairly high level of student satisfaction with the study of natural sciences and humanities online.
An Improved Gray Wolf Optimization Algorithm Based on Levy Flight and Adaptive Strategies

A new improved gray wolf optimization algorithm (LAGWO) is proposed to address the disadvantages of global exploration and local exploitation imbalance, slow convergence speed, low optimization-seeking accuracy and easy to fall into local optimality when solving complex problems. Firstly, the influence of the attenuation factor on the gray wolf optimization algorithm is analyzed, and an adaptive attenuation factor with different exploration ratios can be set according to different optimization problems is proposed to balance the exploration and exploitation capabilities of the algorithm and to ensure that the algorithm has a certain global search capability even at the late stage of the optimization search. Numerical simulation experiments show that increasing the exploration capacity ratio is beneficial to improving the convergence accuracy of the algorithm. Then, the characteristics of occasional long-distance walking of Levy's flight are applied to the optimization search process of α and β wolves to improve the global search ability of the algorithm and avoid falling into local optimum. Aiming at the feature that the candidate wolves ignore the different importance of the three leading wolves in the position update, the adaptive learning weight strategy is proposed to ensure that the constraint of individual gray wolves is reduced at the early stage of the algorithm seeking and improve the global search ability of the algorithm, and at the same time, it can speed up the convergence speed and improve the convergence accuracy at the late stage of the seeking. Finally, simulation experiments are carried out for 12 standard test functions and compared with several other algorithms, and the experimental results show that the algorithm has greater advantages in the optimization-seeking accuracy, algorithm stability and convergence speed.
Parallel Algorithm of Geomagnetic Localization in Fire Compartment Based on MapReduce

All Traditional geomagnetic fingerprint location is characterized by serial algorithm, which is realized through pair-wise comparison in in-door Geomagnetic big data which is generated by earth's magnetic field. The system is bottle-necked when processing massive smart phone location data. In this paper, a new Hadoop-MapReduce based method is put forward, which can be seen as a parallel algorithm. Through introducing more computers, the new method can greatly enhance the performance of in-door geomagnetic localization. In the meantime, a dynamic spatial-temporal window is used to promote the detecting accuracy.
Cryptography model to secure IoT device endpoints, based on polymorphic cipher OTP

The security of data exchange between IoT components is of vital importance to avoid integrity breaches in M2M relationships.The main reason for incorporating complex encryption strategies is the processing and memory limitations of embedded systems. This paper proposes an efficient alternative for encrypting messages between two IoT components with processes that make use of keys that change with each new communication making it difficult for brute force attacks to gain access to the exchanged content. Additionally, the method adds a mutation of the encryption functions that adds another additional difficulty to decrypt the message since it depends not only on the key but also on the sequence of application of the decryption functions. The algorithm can be scaled up to end nodes with larger resources, so that the number of bits of the keys and the complexity of the encryption functions can be further increased, and since it is applied directly on the payload, it is independent of any other security method used in higher layers.
Secure YOLOv3-SPP: Edge-Cooperative Privacy-preserving Object Detection for Connected Autonomous Vehicles

The connected autonomous vehicles (CAVs) are a key component of intelligent transportation systems (ITS) where vehicles communicate with each other to exchange sensing data from on-board sensors (e.g., high-definition cameras and lidar). For the sake of the category and location privacy leakage of sensing images shared by CAVs and computational inefficiency of privacy-preserving object detection framework in edge computing environment, we propose a lightweight and privacy-preserving detection framework (PPDF) to support secure extraction, classification and detection of image features, and achieve the goal of data transmission and computing security under the collaborative detection process of edge nodes. Particularly, we design a secure clustering prediction protocol of object anchors, a secure object classification and regression protocol, secure object upsampling and secure feature fusion protocols. Finally, PPDF based on edge-cooperation was constructed and two non-collusive edge servers were used to perform PPDF. Theoretical analysis of correctness, security and complexity indicate that PPDF can not only realize correctness of object detection, but also let category and location privacy of images are protected effectively and have excellent accuracy. Actual performance evaluation shows that PPDF can achieve the same detection accuracy as original YOLOv3-SPP model. At the same time, compared with homomorphic encryption and multi-round iterative calculation schemes, PPDF has obvious advantages in terms of computational cost and communication overhead.
Implementation of Data Science Techniques in the ACM Computing Classification System

In 2012, ACM defined a Computing Classification System (CCS), which substitutes an older version developed in 1998. This CCS relies on a semantic vocabulary as the single source of categories and concepts that reflect the state of the art of the computing discipline. This paper shows the application to the CCS of two Data Science techniques used for classification tasks: Naive Bayes (NB) and Convolutional Neural Network (CNN). The project comprises two tasks: developing a framework with the general roles and data structure and applying NB and CNN to CCS classification. After the adjustment of some model parameters, the results are promising. The classification with NB provides an accuracy of 74%, while the accuracy with CNN was 72%.
A Deep Learning Approach to Recognize Mixed Fonts Printed Arabic Characters

There is an immense need for recognition systems that rely on Arabic characters to provide a reliable and fast processing of data. Since Arabic writing is widely used in various real-world applications, this motivated us to develop a recognition system which recognizes mixed fonts printed Arabic letters of different sizes besides ligatures, digits, and punctuation marks. The proposed system consists of the preprocessing phase, the feature extraction phase, and the recognition phase which exploiting two models of Convolutional Neural Networks CNNs to recognize the characters. The experimental results are very promising as the second model (CNN model 2) outperforms the first model (CNN model1) and achieves an accuracy rate of 99.86%.
Design of an easy upgradable cost efficient autonomous assistive robot ROSWITHA

In this paper, a design and development of an assistive robot are proposed that assists to the elderly and handicapped people, such as providing information and care with daily routine supports with the ability to perform various tasks, including the contact safety. Firstly a clear function requirement for such assistive robots was made. Secondly different sub-parts have been mounted to synthesize/unify the functions such as high power, dexterity/competence, mobility, safety and the cost-effectiveness. Additionally, a set of different applications, like the robot moves to other rooms/locations according to the commands given by the user, and the arm performs the task of grabbing and pouring/serving. Based on the application, the assistive prototype robot ROSWITHA (RObot System WITH Autonomy) was developed successfully, that operates using three major systems- the vision system, the arm system and the navigation system. The mechanisms are the camera, LIDAR, ultrasonic sensors, dual arm with gripper and two wheeled mobile vehicle including two castor wheels. Some experiments are performed focusing on assistive tasks using ROSWITHA's sub-systems, indicating that the robot software can be integrated and become extremely useful to improve the living style of the elderly and handicap people in future.
Local Polynomial Models for Real-Time Tracking of Humidity Sensor Signals

Sensor signal tracking has great practical importance for solving a number of problems such as data fusion and sensor integration in multi-sensor systems (MSS), robotics, fault detection (FD), and diagnosis, etc. The Kalman filter is a widely used algorithm that can be applied in these cases if an appropriate model of the signal is available. The present study shows that under a step change in humidity, the filter can estimate the signal from a humidity sensor by means of a second-order local polynomial model. This gives a reason to expect similar behavior not only for sudden but also for all other changes. The proposed online signal tracking solution is simple to implement and can be used not only for humidity sensors, but also for sensors that measure quantities such as temperature, pressure, level, etc.
Fuzzy Expert Forecasting System for Coconut Scale Insect Infestation

The purpose of this study is to develop a coconut scale insect infestation forecasting system for Philippine Coconut Authority in Southern Luzon, Philippines. The system was developed using Sugeno-style fuzzy inference in MatLab and realized in MS Excel VBA Macro. The study examined the effect of weather parameters such as temperature, relative humidity, and wind speed on coconut scale insect infestation and predicted the potential future degree of infestation. The Fuzzy Inference System technique applied in this study includes the use of a triangular membership function, the formulation of 27 If-Then rules, the use of AND logic operator, and the weighted average defuzzification. The designed system was tested by comparing recorded data from the 2014 CSI infestation in five provinces of Southern Luzon, Philippines, to the data generated by the developed software. The actual infestation and the data generated by the developed GUI differed by 1 to 5%. Experts believe that this is due to the government's intervention to address the CSI problem. Furthermore, temperature has a 45% impact on the infestation, followed by wind speed (30%) and relative humidity (25 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup> ). The overall system evaluation result is 4.1, which is interpreted verbally as very good. As a result, it was concluded that the applied fuzzy logic concept, which is developed into VBA Macro, is a useful tool for forecasting the severity of CSI infestations.
Residual Attention Network: A new baseline model for visual question answering

Answering questions over images is a challenging task, it requires reasoning over both images and text. In this paper, we introduce Residual Attention Network(RAN), a new visual question answering model, and compare it with baseline models such as stacked attention model and CNN-LSTM model. We find that our model performs better than these baseline models. In addition to our model, we also evaluate several holistic models and compare them with neural module networks frameworks, and the results show that neural modules networks perform better in questions reasoning. All the experiments have been done on the CLEVER dataset, which is a recent VQA dataset for evaluating multiple-step reasoning VQA models.
Multi-turn Query with Similarity Feedback Facilitates Multimodal Video Clip Retrieval

Following the rise of internet, multimedia applications have become popular and been expanded rapidly. How to retrieve the user-specified video content is an important issue. Since the query varies from user to user for the same content, one turn search usually cannot find the satisfying answer. Therefore, we extend the typical single-turn multimodal video clip retrieval to a multi-turn system. To pull this off, video data are transformed into feature vectors. At each turn, the user inputs a query sentence to the system, and the system will output six candidate video clips accordingly. The system recommends video clips based on the results of a clustering algorithm and video retrievals benefit from the similarity feedback of previous turn. The results show that the proposed method outperformed the single-turn retrieval on the metrics of recall and the similarity.
Removing Watermarks For Image Processing Networks Via Referenced Subspace Attention

Abstract Deep neural network model extraction attack is the process of retraining a surrogate model based on the outputs of a target model with a given set of inputs. Such attacks are hard to defend for the sake of model owners’ interest. Recently, some work propose model watermarking scheme for image processing networks, which is able to prove the intellectual property of deep models even after the model extraction attack. This scheme makes sure that, once the target model (an image processing network) is watermarked, we can extract the watermark from the output of the surrogate model. In this paper, we propose a new model extraction attack scheme to fight against the latest method. Instead of directly using the output images of a target model, we propose to use their reconstructed versions for model retraining, where an asymmetrical UNet is proposed for image reconstruction. To thoroughly remove the watermarking traces, we propose and incorporate a referenced subspace attention module in the asymmetrical UNet, which removes the watermark by projecting the outputs of the target model into the subspaces of the reference image. Various experiments demonstrate the effectiveness of our attack.
Hand Gestures-Based Smooth 3D Trajectories Computation Applied to Real-Time Drone Control by Tracking 2D Hand Landmarks

Robotic systems are increasingly being adopted for filming and photography purposes. In fact, by exploiting robots, it is possible to program complex motions, achieving high-quality videos and photographs. The contribution of the presented paper is an alternative approach for robot motion planning w.r.t. the joystick. Starting from a state-of-the-art hand tracking system, to detect 2D hand reference points, we built a pipeline to reconstruct 3D trajectories. To evaluate the proposed remote control approach, a captured trajectory is tested in a simulation environment to control the motion of a drone. In addition, experiments are provided using the DJI Ryze Tello drone to prove the feasibility of the approach in real conditions.
Investigation of Credit Card Fraud detection under the lens of Comparative Machine Learning models

Fraudulent use of credit cards is common and may result in significant financial losses. Using Trojans or Phishing, criminals may get the credit card numbers of innocent victims for their criminal acts. As a result, it is vital to have fraud detection technology that can catch a thief in the act of purchasing using a stolen credit card. One solution is to utilize machine learning algorithms to construct normal/fraud behavior characteristics from all historical transaction data, including legitimate and fraudulent ones, and then use these features to identify whether a transaction is fraudulent or not. In this paper, SVC, Multinomial Naïve Bayes, K-Neighbors Classifier, Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM algorithms were used for credit card fraud detection. The detailed results of all seven algorithms were studied. Then a comparison between these seven algorithms with their precise accuracy, ROC curve, train-test, validation scores, and the learning curve was discussed. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM showed 100% accuracy in training dataset. While Random Forest algorithm outdid every other algorithm by scoring 100% for both train and test data, the result of the remaining algorithms for the train and test data were also satisfactorily ranging from 75.3% to 99.6%. Logistic Regression, Random Forest, Bernoulli Naïve Bayes, and SVM all 4 scored 100% in both train and test data, and Random Forest scored 100% leaving behind all the algorithms. Logistic Regression, Bernoulli Naïve Bayes and SVM scored 99.6% in test. The best-performing algorithm was selected according to its performance and discussed its AUC score and confusion matrix.
Looking at the stars in the twenty-first century: Lotman's semiotics, progress and utopia

In the article, we consider special cases of technological progress and social dystopia studied by Yuri Lotman from the point of view of the inhabitants of the first quarter of the twenty-first century. The analysis is illustrated by works of literary fiction: Kazuo Ishiguro's dystopian novels Never Let Me Go (2005) and Klara and the Sun (2021).
AI-powered automatic detection of dynamic triggering of earthquake based on microseismic monitoring

Detection of dynamic triggering of earthquakes combined with microseismic monitoring allows a better understanding on rock damage process in rock engineering, which involve local stress disturbances caused by far-field earthquakes. However, rapid and efficient detection of dynamic triggering of earthquakes remain clueless due to the lack of enough investigation. This study proposed a novel method for automatic detection of dynamic triggering of earthquakes based on convolutional neural networks (CNNs). Results show that the trained model is capable of detecting earthquakes with a high accuracy rate of 98.3%, which provided strong supports for the automatic detection of dynamic triggering of earthquakes. This method is applied to the Xiaojiang fault region, and dynamically triggered earthquakes were spotted for 31 of the 47 selected earthquakes. Our method achieves 94% recognition accuracy on the detection of dynamic triggering of earthquakes. This method offers a rapid and accurate framework to detect dynamic triggering of earthquakes and investigate the damage to rock engineering structures caused by far-field earthquakes based on microseismic monitoring.