Reviewerly/exHarmony huggingface dataset:
Image-Based Arabic Sign Language Recognition System Using Transfer Deep Learning Models

Sign language is a unique communication tool helping to bridge the gap between people with hearing impairments and the general public. It holds paramount importance for various communities, as it allows individuals with hearing difficulties to communicate effectively. In sign languages, there are numerous signs, each characterized by differences in hand shapes, hand positions, motions, facial expressions, and body parts used to convey specific meanings. The complexity of visual sign language recognition poses a significant challenge in the computer vision research area. This study presents an Arabic Sign Language recognition (ArSL) system that utilizes convolutional neural networks (CNNs) and several transfer learning models to automatically and accurately identify Arabic Sign Language characters. The dataset used for this study comprises 54,049 images of ArSL letters. The results of this research indicate that InceptionV3 outperformed other pretrained models, achieving a remarkable 100% accuracy score and a 0.00 loss score without overfitting. These impressive performance measures highlight the distinct capabilities of InceptionV3 in recognizing Arabic characters and underscore its robustness against overfitting. This enhances its potential for future research in the field of Arabic Sign Language recognition.
Dynamical Systems

Dynamical systems are mathematical models concerning variables that change depending on time. In this chapter, we consider a system represented by a linear differential equation in which a variable changes deterministically over continuously changing time, and a Markov chain in which a variable changes stochastically with discretely changing time. In studying these systems, the theory of linear algebra, which we have learned so far, will play a major role.
CONVENTION RELATING TO THE DISTRIBUTION OF PROGRAMME-CARRYING SIGNALS TRANSMITTED BY SATELLITE AS AN ANONYMATIC INTERNATIONAL TREATMENT FOR THE PROTECTION OF RELATED RIGHTS

Abstract: the successful launch of an artificial satellite of the Earth, the flight of man into space served as an impetus for the emergence and subsequent development of satellite communication systems for transmitting televi‐ sion signals. At the same time, new means of communication, with a wide range of action, raised the issue of the protection of the rights of broad‐ casting organizations, which was to be resolved in 1974 at an international conference in Brussels. The purpose of the article is to determine the position of the Brussels Convention in the system of law through the prism of historical, functional and objective approaches. The author comes to the conclusion that this convention in relation to the Rome Convention of 1961 is a special treaty concerning the regulation of relations in the field of copyright and related rights. The identified distinctive features of the Brussels Convention make it possible to give it the legal status of an atypical treaty.
Training A Dynamic Neural Network to Detect False Data Injection Attacks Under Multiple Unforeseen Operating Conditions

As a cyber-physical attack targeting power systems, False Data Injection Attack (FDIA) has raised widespread concern in recent years. Many FDIA detection approaches in the literature train learning models using historical data to distinguish attacked measurements from normal ones. These approaches typically suffer from severe generalization problems. Although they can achieve good detection performance when the data has a similar distribution in the offline training and online application phases, they often perform poorly when an online operating condition is unforeseen during offline training. To address this problem, this paper proposes a two-layer Dynamic Neural Network (DyNN) framework for FDIA detection. The lower layer is like a traditional FDIA detection model, which uses a Convolutional Long Short-Term Memory (ConvLSTM) network to extract latent features, and employs a Multi-Layer Perceptron (MLP) to make detection decisions based on the extracted features. However, unlike traditional approaches where the internal parameters of the learning model are fixed after training, the internal parameters of the MLP are dynamically generated according to the online data by an upper-layer DyNN adapter. An interactive co-training mechanism is also proposed to coordinate both layers and ensure that the dynamically generated MLP parameters are best suited for FDIA detection in the current operating condition. Extensive experiments demonstrate that the two-layer DyNN framework can achieve satisfactory FDIA detection performance even though the training and test data are collected under completely different operating conditions. In the most extreme cases, the average accuracy and F1 score of the proposed DyNN-based FDIA detection framework are 4.91% and 4.24% higher than that without DyNN, respectively.
Multi-head Self-attention and BGRU for Online Arabic Grapheme Text Segmentation

The segmentation of online handwritten Arabic text into graphemes/characters is a challenging task for the recognition system due to the nature of this script. That is why, it is better to employ dependency in the context of segments written before and after it, to improve recognition accuracy. In this paper, we introduce Multi-Head Self-Attention (MHSA) and Bidirectional Gated Recurrent Units (BGRU) models for online handwritten Arabic text segmentation, both of which simulate our previous grapheme segmentation model (GSM). The proposed framework consists of word embedding and the combination of complementary Multi-Head Self-Attention and BGRU, which help detect the control points (CPs) for handwritten text segmentation. The CPs delimit each grapheme composed of three main geometric points: starting point (SP), ligature valley point (LVP), angular point (AP), and ending point (EP). To show the effectiveness of our MHSA-BGRU model for online handwritten segmentation and its comparison with GSM, both mean absolute error (MAE), and word error rate (WER) evaluation metrics are used. Experimental results on benchmark ADAB and online-KHATT datasets show the efficiency of our model, which achieves 3.17% and 5.28% for MAE, 12.25% and 25.13% for WER respectively.
Local Learning-based Collaborative Authentication In Edge-Fog Network

In the Edge/Fog network, several issues like mobility support, location awareness and low latency are enabled by offloading cloud resources and services to the fog. However, sending massive data from edge devices to a fog server incurs huge network traffic. Existing security mechanisms in such network are facing several issues due to the lack of real-time decisions and high time delays. In this context, we propose a local learning-based collaborative authentication scheme using Federated Learning (FL) to address the challenge by training models locally in Edge devices and only sharing model parameters with the fog server. In our proposed scheme, the fog brings security mechanisms near Edge devices which reduce communication overhead since the observed authentication information and parameter updates are stored and processed locally by the cooperative edge devices. Moreover, FL enables a privacy-aware collaborative learning while preserving users’ privacy.
Method to Visualize Passes in a Soccer Match by Clustering Players and Positions

In this paper, we propose a method to visualize passes in a soccer match so that defenders can comprehend the tendency of offensive passes. Pass maps, which display passes as line segments, are often used to describe the positions of passer and receiver, but it is not suitable to see the tendencies of players involved in passes. Pass networks are suitable to describe players involved in passes, but it lacks the precise positions of players when passes are executed. To resolve these problems, we developed a method that utilizes cluster analysis integrating players and positions involved in a pass to visualize the tendency of passes. In addition to that, parameters for clustering are editable so that users can generate several visualizing results.
A Model for the Automatic Mixing of Multiple Audio and Video Clips

Short-form videos are becoming a trend in today’s social media. A large number of tools provide users with the capability to edit and mix video clips with audio files to create a short-form video. A significant number of such videos currently exist on platforms such as TikTok and Instagram that were created by manual cut and mix of video and audio clips. In this paper, we examine the relationship that exists between video scenes and audio clips in order to identify the best mixing strategy of multiple video and audio files. For this purpose, we extract the beats and downbeats of the audio files and augment them with the scene changes in the video files. Next, we use the data to train two LSTM and RNN models to determine the best places to cut the video based on the audio data. Our system enables users to automatically create high-quality short-form videos by simply specifying the video clips and the audio file that should be mixed with them. We conducted experiments on 200 short-form videos. Our results illustrate the efficiency of the proposed system in determining the optimal cut scenes based on the characteristics of the selected audio file.
A Selective Video Encryption Scheme Based on a Block Cipher Algorithm and a Chaos Generator

In the context of today’s rapidly changing world, multimedia data have been used more and more widely in human being’s life. Large amounts of multimedia data have been produced and diffused in several fields. Thus, various encryption approaches for multimedia applications and services have been proposed in order to gratify different applications. Many video encryption algorithms combine encryption within the video coding algorithm to satisfy both video compression and encryption. In fact, the H.264 Advanced Video Coding (H.264/AVC) standard is one of the most frequently used formats for video compression. In this paper, we propose a novel selective encryption method that combines two encryption techniques and deals with the requirements of the real-time applications. The proposed scheme achieves video encryption using a chaos key generator and the AES (Advanced Encryption Standard) algorithm. To evaluate the proposed scenarios, we calculated the hardware performances and conducted five different security analyses.
Focus on Physics: A Personal Journey to Classroom Success

"Focus on Physics: A Personal Journey to Classroom Success." The Science Teacher, 89(3), pp. 14–15 Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, new 13th edition; Conceptual Physical Science, 6th edition, coauthored with Leslie Hewitt and John Suchocki; and Conceptual Integrated Science, 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
For Your Eyes Only

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsZeynep Gonca AkdemirZeynep Gonca Akdemir (zakdemir@purdue.edu) is a PhD student in Science Education, Muhsin Menekse is an Assistant Professor of Engineering and Science Education, Mahdi Hosseini is an Assistant Professor of Electrical & Computer Engineering, Arindam Nandi is a PhD student in Electrical & Computer Engineering, and Keiichiro Furuya is a PhD student in Physics, all at Purdue University, West Lafayette, IN.Muhsin MenekseZeynep Gonca Akdemir (zakdemir@purdue.edu) is a PhD student in Science Education, Muhsin Menekse is an Assistant Professor of Engineering and Science Education, Mahdi Hosseini is an Assistant Professor of Electrical & Computer Engineering, Arindam Nandi is a PhD student in Electrical & Computer Engineering, and Keiichiro Furuya is a PhD student in Physics, all at Purdue University, West Lafayette, IN.Mahdi HosseiniZeynep Gonca Akdemir (zakdemir@purdue.edu) is a PhD student in Science Education, Muhsin Menekse is an Assistant Professor of Engineering and Science Education, Mahdi Hosseini is an Assistant Professor of Electrical & Computer Engineering, Arindam Nandi is a PhD student in Electrical & Computer Engineering, and Keiichiro Furuya is a PhD student in Physics, all at Purdue University, West Lafayette, IN.Arindam NandiZeynep Gonca Akdemir (zakdemir@purdue.edu) is a PhD student in Science Education, Muhsin Menekse is an Assistant Professor of Engineering and Science Education, Mahdi Hosseini is an Assistant Professor of Electrical & Computer Engineering, Arindam Nandi is a PhD student in Electrical & Computer Engineering, and Keiichiro Furuya is a PhD student in Physics, all at Purdue University, West Lafayette, IN.Keiichiro FuruyaZeynep Gonca Akdemir (zakdemir@purdue.edu) is a PhD student in Science Education, Muhsin Menekse is an Assistant Professor of Engineering and Science Education, Mahdi Hosseini is an Assistant Professor of Electrical & Computer Engineering, Arindam Nandi is a PhD student in Electrical & Computer Engineering, and Keiichiro Furuya is a PhD student in Physics, all at Purdue University, West Lafayette, IN.
FaceAnonym: Face Anonymization Model via Latent Space Mapping

Machine learning has become a key driver of technological development, but the rising demand for AI applications involving human interaction necessitates access to large databases of human image data. However, the use of large real-world image datasets, particularly those that contain faces, has given rise to valid privacy concerns. We examine the critical issue of anonymizing image datasets that contain facial information in this paper. We hope to strike a balance between the requirement for data-driven advancements and preserving people’s right to privacy by addressing these issues. In this paper, we propose a new method named FaceAnonym that de-identifies facial images by projecting them onto the latent space of a GAN model. This allows us to preserve the important characteristics of the face, such as shape, expression, and luminance, while still obscuring the identity of the individual. Finally, our method has been shown to be more effective than other methods at de-identifying facial images. It is also fast and easy to use, making it a practical solution for de-identifying large datasets of facial images.
Evaluating Light Probe Estimation Techniques for Mobile Augmented Reality

Realistic lighting approaches typically rely on physically-based rendering which in turn often makes use of image-based lighting. Enabling these techniques in augmented reality on mobile devices requires unique approaches to estimate light probes, given the limited camera and sensor data available. In this paper, we evaluate different time-dependent and time-independent techniques for light probe estimation in augmented reality applications that try to predict the environment lighting using single images or video streams in combination with inpainting techniques. We simulate real-world applications using an evaluation framework where a simulated mobile device captures camera streams from different scenarios following a pre-defined path. The resulting camera streams are fed to a total of six estimation techniques in order to create light probes which are then used to render virtual objects while applying various materials. By comparing the rendered images as well as the light probe estimations, we perform a quantitative evaluation. We show how approaches that are able to process continuous video streams can provide more plausible results in cases where sufficient camera movement is present. Additionally, we investigate the visual impression of different types of materials showing that rough surfaces with distinct colors are less likely to produce divergent estimation results.
Remote Cross-platform Instructions between MR and VR

This paper introduces a collaboration setting where an expert in VR gives instructions to a technician using MR being in front of the real object. For this study, we develop a system in which users communicate while building a specific structure using Lego Duplo blocks. Here, only the expert knows the final configuration and instructs the technician equipped with an MR headset about the assembly. Four communication methods are evaluated: voice chat, avatars, the possibility of moving virtual blocks similar to the real ones, and an arrow functionality. This work proves the usefulness of such a VR/MR instruction system and provides insights into the advantages of each proposed communication method.
Exploring Continual Learning and Self-learning for Historical Digit Recognition

Self-learning has demonstrated remarkable performance in computer vision tasks. However, dealing with unlabeled data that needs to be recognized based on previously acquired knowledge is crucial. Specifically, in Continual Learning where new data is incrementally learned, without the need for training from scratch, models often suffer from Catastrophic forgetting. In this study, we investigate the potential of continual self-learning, using LeNet architecture, to address the issue of learning and recognizing unlabeled historical digits by transferring the knowledge gained from the Split-MNIST dataset. The application of Continual self-learning to unlabeled historical digits unlocks the potential for machines to gain a deeper understanding of our digit-based history and provide increasingly insightful interpretations. To the best of our knowledge, this is the first work that investigates Continual Learning in the context of historical handwritten text recognition.
Smart Blockchain-based Authorization for Social Internet of Things

The Social Internet of Things represents an innovative paradigm which empowers sharing the data and services provided by the surrounding IoT devices. To access the shared resources, an IoT device needs to either hold its own permission or get the required permission by delegation. Since it has a large number of social friends, the device can obtain the required permission by delegation if its friends have the right to delegate their permissions. As a consequence, the evaluation of numerous delegation policies generates a lot of access policies associated with the delegated permission, leading to the requirement for choosing the delegations to perform. However, the traditional delegation process does not support the selection of the delegations. In this paper, we propose to extend the functionalities of the access control mechanism by integrating a new functionality to the delegation process. This new functionality allows to select the appropriate delegations based on predefined delegatee rules or machine learning classification model with the consideration of the delegator characteristics, features of the social relationship linking the delegator to the delegatee and delegation history of the delegator. The simulation results show that the smart selection can reduce the delegation overhead while it guarantees multiple delegation choices.
Shape Attention Module for Regularizing 3D Face Reconstruction

Existing 3D face reconstruction methods can recover geometric faces in the general case. However, they ignore the impact of expression variation on shape capturing. To the best of our knowledge, this is the first work that refines the shape from the coupled relationship of expression and shape. We design a Shape Attention Module to extract the shape information in the high-dimension space, which is then remapped back to a lower-dimension space. In addition, we propose Confidence Landmarks Loss based on the fact that different face landmarks have different confidence levels. Lastly, we design an auxiliary network to regress the confidence values of 68 landmarks. We qualitatively and quantitatively test our method on NoW and AFLW2000-3D datasets. Extensive experimental results demonstrate that our method achieves better performance than prior arts.
Non-Playable Character Movement Controls For Tour Guides And Tourist Activity Simulation Using Artificial Bee Colonies

Indonesia has various wonderful location, makes it a tourist destination. The large number of tourist sites makes potential visitors need to conduct initial information searches for having expectations of the site. One of the information that can be used is a tourist simulation using interactive media for matching expectation with the experience. Simulation of the mass tourist with tour guide may encounter error when a tourist of tourist group collided with another tourists or object in the game environment while following the tour guide. This study discusses the implementation of a tourist group with tour guide simulation enabled by artificial intelligence. The Artificial Bee Colony algorithm is used in this study to prevent Tourists as Non-playable characters (NPC) from colliding with surrounding objects and other NPCs. The results of this study show that the success rate for NPCs in avoiding other NPCs is 100%, NPCs in avoiding objects around them is 89%, and NPCs reach the target point by 80%.
Fine Multi-View Feature Selection

With the rapid development of computer science, high-dimensional multi-view data has presented which contains more noise and redundant information and is not conducive to multi-view learning task. It is necessary to reduce the dimension of multi-view data. Multi-view feature selection is a common method of dimensionality reduction for multi-view data, which can effectively eliminate noise and redundant information. In addition, multi-view feature selection requires further mining consistency information and complementary information of multi-view data. To solve above problems, we propose a fine multi-view feature selection method based on supervised environment. The proposed method introduces <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$l_{2,0}$</tex> -norm to achieve fine feature selection. Further, the proposed method introduces two adaptive weighting parameter learning schemes to mine the correlation information between views and categories. This paper designs an effective alternating iterative optimization algorithm. Experiments on six multi-view datasets demonstrate the effectiveness and superiority of the proposed method.
Adversarial Label Flips Attack to Decrease the Security of SVM Against Evasion Attack

Pattern recognition has been employed in many security-sensitive applications, e.g., malware detection and intrusion detection, to detect malicious samples. One of the most popular adversarial attacks is evasion attack, which deliberately modifies malicious samples in order to evade the detection of a deployed pattern recognition system at test time. However, for the malicious samples far away from the decision boundary of a classifier, a successful evasion attack is of high cost, or even impossible. In this paper, we propose a label flips attack to mislead the learning process of a classifier, aiming to decrease the security of a classifier against evasion attack, i.e., making the evasion attack much easier. The attack samples are greedily selected to minimize the security of a classifier, which is measured by the expected least cost of successful evasion attacks for the malicious samples. We conducted experiments on spam filtering and mal ware detection and showed that the proposed label flips attack greatly decreased the security of support vector machines against evasion attack. An attacker can not only conduct a successful evasion attack with less cost, but also achieve higher success rate within the given capability. In order to speed up the proposed label flips attack, we devised a pre-processing technique for selecting the candidate training samples based on a kernel density estimator (KDE). This paper discovers a potential threat to machine learning algorithms, which is of great important for developing a secure system.