Reviewerly/exHarmony huggingface dataset:
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages

It is challenging to train and deploy Transformer Language Models (LMs) for hybrid speech recognition second pass re-ranking in low-resource languages due to (1) data scarcity in low-resource languages, (2) expensive computing costs for training and refreshing 100+ monolingual models, and (3) hosting inefficiency considering sparse traffic. In this study, we present a novel way to group multiple low-resource locales together and optimize the performance of Multilingual Transformer LMs in ASR. Our Locale-group Multilingual Transformer LMs outperform traditional multilingual LMs along with reducing maintenance costs and operating expenses. Further, for high-traffic locales where deploying monolingual models is feasible, we show that fine-tuning our locale-group multilingual LMs produces better monolingual LM candidates than baseline monolingual LMs.
Defending Against Targeted Poisoning Attacks in Federated Learning

Federated learning (FL) enables multiple participants to collaboratively train a deep neural network (DNN) model. To combat malicious participants in FL, Byzantine-resilient aggregation rules (AGRs) have been developed. However, although Byzantine-resilient AGRs are effective against untargeted attacks, they become suboptimal when attacks are stealthy and targeted. In this paper, we study the problem of defending against targeted data poisoning attacks in FL and make three main contributions. First, we propose a method for selective extraction of DNN parameters from FL participants’ update vectors that are indicative of attack, and embedding them into low-dimensional latent space. We show that the effectiveness of Byzantine-resilient AGRs such as Trimmed Mean and Krum can be improved if they are used in combination with our proposed method. Second, we develop a clustering-based defense using X-Means for separating items into malicious versus benign clusters in latent space. Such separation allows identification of malicious versus benign updates. Third, using the separation from the previous step, we show that a "clean" model (i.e., a model that is not negatively impacted by the attack) can be trained using only the benign updates. We experimentally evaluate our defense methods on Fashion-MNIST and CIFAR-10 datasets. Results show that our methods can achieve up to 95% true positive rate and 99% accuracy in malicious update identification across various settings. In addition, the clean models trained using our approach achieve similar accuracy compared to a baseline scenario without poisoning.
Detecting Cloud-Based Phishing Attacks by Combining Deep Learning Models

Web-based phishing attacks nowadays exploit popular cloud web hosting services and apps such as Google Sites and Typeform for hosting their attacks. Since these attacks originate from reputable domains and IP addresses of the cloud services, traditional phishing detection methods such as IP reputation monitoring and blacklisting are not very effective. Here we investigate the effectiveness of deep learning models in detecting this class of cloud-based phishing attacks. Specifically, we evaluate deep learning models for three phishing detection methods–LSTM model for URL analysis, YOLOv2 model for logo analysis, and triplet network model for visual similarity analysis. We train the models using well-known datasets and test their performance on cloud-based phishing attacks in the wild. Our results qualitatively explain why the models succeed or fail. Furthermore, our results highlight how combining results from the individual models can improve the effectiveness of detecting cloud-based phishing attacks.
The Role of Data Mining Tools in Commercial Banks' Cyber-Risk Management

This study explored the data mining tools role in cyber-risk management among Jordanian commercial banks listed on the Amman Stock Exchange. A standardized questionnaire was distributed to the information technology and bank risk management staff (n=139) in 13 commercial banks. There were significant effects of data mining tools in cyber-risk management on the discovery, dissemination, and participation of knowledge systems, and on opportunities to improve knowledge systems through the development of the environment for study and data retrieval systems. This study emphasizes the need to look for areas of improvement that would affect the efficiency of using data mining tools in the discovery, dissemination, and sharing of knowledge systems to manage cyber risks, as well as the need to combine data mining technology with to use their cognitive abilities and technical concepts with the necessary methods and tools to improve the bank's knowledge culture and to provide all necessary data stores and warehouses for the formation of new knowledge, which would help in the development of cyber risk management in Jordanian banks, which are listed on the Amman Stock Exchange.
Virtual Reality and Teaching Paradigms

Nowadays, virtual world technologies have been incorporated into various types of higher educational programs, which are frequently accompanied by enthusiastic claims that students improve their ability to experience learning problems and tasks in the computer-mediated virtual reality (VR) using computer-generated agents or virtual avatars. Because avatar interactions are the most important learning experiences, the possibility of unstable avatar identities, as well as the ambiguous relationship between avatars and the learners who control avatars have caused serious problems. Furthermore, considering the unknown transfer of experiential learning from the virtual world to the real world, the final question is the possibility of avatars engaging in unethical behavior. Reflecting on such concerns, this study attempts to connect the use of virtual avatars with five main experiential learning paradigms: constructivism, psychoanalytic interference, situative learning, critical cultural liberation, and co-emergence complexity. The results shows that the application of virtual world technologies for educational purposes is still in its infancy, and it is also at a stage full of expectations and enthusiasm.
Research on Virtual Skiing System based on Harmonious Human-computer Interaction

Based on the human-computer and in view of the current problems of short skiing cycle and easy to cause sports injuries when carrying out skiing sports, this study designs and develops a virtual skiing system for harmonious human-computer interaction. Firstly, according to the design principles of the virtual skiing system, a system construction method integrating hardware and software is proposed. After that, the human-computer interaction system was designed to provide a personalized and customized interface to achieve motion interaction with the virtual ski character based on the user's real-time dynamic measurement data, and to provide a responsive environment for the user by controlling the head vision, hand movements and lower limb movements of the virtual character through hardware devices respectively, and designing multimodal sensory feedback. Finally, the system was tested, and the results showed that the system is operable and has a certain sense of immersion.
MIND-VR: A Utility Approach of Human-Computer Interaction in Virtual Space based on Autonomous Consciousness

Virtual reality(VR) is developing rapidly and has broad prospects, but the current VR is not separated from keyboard and mouse operation. It lacks immersion and can not directly reflect the subjective wishes of users. Inspired by the development of hybrid human-computer interaction technology, this paper studies a practical interaction technology MIND-VR based on Brain Computer Interface(BCI) and eye tracker. The method has low difficulty and good effect. The validation shows that the interactive effect can meet the needs of users. Increase the immersion of users' free interactive experience. The MIND-VR interaction mode proposed in this paper is a meaningful exploration of human-computer interface technology based on consciousness control. It is of great significance for human-computer interaction in virtual space and the promotion of BCI and VR.
A Pedestrian Multi-object Tracking Algorithm based on CenterTrack for Autonomous Driving

Multi-object tracking is a research hotspot in intelligent driving scenarios, and the trajectory association of tracked objects is one of the hotspots that needs to be solved urgently. The CenterTrack algorithm using the “joint detection and tracking” paradigm has high inference speed and accuracy. However, object overlap due to accidental occlusion and scene clutter often leads to missed detections and tracking failures. In response to the above problems, this paper proposes a multi-object tracking model SPCTrack, which combines the attention mechanism, multi-scale fusion and other ideas, and uses pyramid segmentation attention model to improve the detection effect of the network when the target is occluded. In addition, this paper also introduces the Swish activation function to effectively improve the correlation accuracy during tracking. This paper is validated on the MOT16 and MOT17 datasets, the results show that compared with CenterTrack and other mainstream tracking models, SPCTrack improves the detection accuracy, association accuracy, and overall tracking accuracy of multi-object tracking. At the same time, the speed can reach 19.1FPS, with high real-time performance.
Application of Configuration Graphics Modeling Technology in Scheduling Automation Graphics System

In dispatching automation monitoring system, graphics system is the appearance of the user interaction with the dispatching automation system, it is beautiful, friendly, easy to extend the features such as direct impact on user acceptance of the whole system. Aiming at the shortcomings of traditional scheduling automation system in component expansion, application secondary development, script automatic control and so on. In this paper, a configuration graphical modeling technology based on JavaScript script and C++ is proposed, and a new scheduling automation human-computer interaction software is designed and implemented, which solves the shortcomings of traditional scheduling automation human-computer interaction system.
An interactive algorithm for augmented reality experiment

In this paper, an interactive algorithm (AREI) for AR experiment is designed. The main innovations of this method are: (1) combining gesture, hand position and sound as multimodal input, the robustness of virtual experiment operation is improved; (2) It solves the coordinate mapping relationship between real environment and virtual space, and improves the accuracy of users using real hands to operate virtual experiments. First, we train the gesture recognition model through Alexnet, and build the speech database through keyword extraction, Through AREI algorithm, the mapping relationship between hand position and virtual scene is determined, and with voice and gestures, users can complete the operation of AR experiment. After testing, the success rate of the experiment through this algorithm is 97.6%, which proves the effectiveness of this algorithm.
Dual Image Cryptosystem Using Henon Map and Discrete Fourier Transform

This paper introduces an efficient image cryptography system. The proposed image cryptography system is based on employing the two-dimensional (2D) chaotic henon map (CHM) in the Discrete Fourier Transform (DFT). The proposed DFT-based CHM image cryptography has two procedures which are the encryption and decryption procedures. In the proposed DFT-based CHM image cryptography, the confusion is employed using the CHM while the diffusion is realized using the DFT. So, the proposed DFT-based CHM image cryptography achieves both confusion and diffusion characteristics. The encryption procedure starts by applying the DFT on the image then the DFT transformed image is scrambled using the CHM and the inverse DFT is applied to get the finally encrypted image. The decryption procedure follows the inverse procedure of encryption. The proposed DFT-based CHM image cryptography system is examined using a set of security tests like statistical tests, entropy tests, differential tests, and sensitivity tests. The obtained results confirm and ensure the superiority of the proposed DFT-based CHM image cryptography system. These outcomes encourage the employment of the proposed DFT-based CHM image cryptography system in real-time image and video applications.
Machine Learning in Modern World

Today the world is totally dependent on technology. Machine learning is one of the game changing technologies which is used in our day to day life. Everyday in machine learning technology there are developed a variety of new products and applications using various machine learning algorithms. Mainly of three types of algorithm. A few applications of machine learning are Product recommendations, Speech recognition etc… The machine learning outcome of the product is perfectly excellent and more accurate. Machine learning is otherwise a subset of Artificial inteligence,it implements several tasks left out any changes. There are many more trending technologies depending on machine learning, in machine learning the process is more effective and quick and faster than human work efficiency. These trends are done by using huge amounts of biological data, medical data,algorithms and numerical models available today. It is also becoming very useful in the upcoming future generation.
Multi-Path Attention Inverse Discrimination Network for Offline Signature Verification

Signature verification, which is a method to distinguish the authenticity of signature images, is a biometric verification technique that can effectively reduce the risk of forged signatures in financial, legal, and other business environments. However, compared with ordinary images, signature images have the following characteristics: First, the strokes are slim, i.e., there is less effective information. Second, the signature changes slightly with the time, place, and mood of the signer, i.e., it has high intraclass differences. These challenges lead to the low accuracy of the existing methods based on convolutional neural networks (CNN). This study proposes an end-to-end multi-path attention inverse discrimination network that focuses on the signature stroke parts to extract features by reversing the foreground and background of signature images, which effectively solves the problem of little effective information. To solve the problem of high intraclass variability of signature images, we add multi-path attention modules between discriminative streams and inverse streams to enhance the discriminative features of signature images. Moreover, a multi-path discrimination loss function is proposed, which does not require the feature representation of the samples with the same class label to be infinitely close, as long as the gap between inter-class distance and the intra-class distance is bigger than the set classification threshold, which radically resolves the problem of high intra-class difference of signature images. In addition, this loss can also spur the network to explore the detailed information on the stroke parts, such as the crossing, thickness, and connection of strokes. We respectively tested on CEDAR, BHSig-Bengali, BHSig-Hindi, and GPDS Synthetic datasets with accuracies of 100%, 96.24%, 93.86%, and 83.72%, which are more accurate than existing signature verification methods. This is more helpful to the task of signature authentication in justice and finance.
Real-Time Indoor Path Planning Using Object Detection for Autonomous Flying Robots

Unknown closed spaces are a big challenge for the navigation of robots since there are no global and pre-defined positioning options in the area. One of the simplest and most efficient algorithms, the artificial potential field algorithm (APF), may provide real-time navigation in those places but fall into local minimum in some cases. To overcome this problem and to present alternative escape routes for a robot, possible crossing points in buildings may be detected by using object detection and included in the path planning algorithm. This study utilized a proposed sensor fusion method and an improved object classification method for detecting windows, doors, and stairs in buildings and these objects were classified as valid or invalid for the path planning algorithm. The performance of the approach was evaluated in a simulated environment with a quadrotor that was equipped with camera and laser imaging detection and ranging (LIDAR) sensors to navigate through an unknown closed space and reach a desired goal point. Inclusion of crossing points allows the robot to escape from areas where it is congested. The navigation of the robot has been tested in different scenarios based on the proposed path planning algorithm and compared with other improved APF methods. The results showed that the improved APF methods and the methods reinforced with other path planning algorithms were similar in performance with the proposed method for the same goals in the same room. For the goals outside the current room, traditional APF methods were quite unsuccessful in reaching the goals. Even though improved methods were able to reach some outside targets, the proposed method gave approximately 17% better results than the most successful example in achieving targets outside the current room. The proposed method can also work in real-time to discover a building and navigate between rooms.
Improved Fruitfly Optimization with Stacked Residual Deep Learning Based Email Classification

Applied linguistics means a wide range of actions which include addressing a few language-based problems or solving some language-based concerns. Emails stay in the leading positions for business as well as personal use. This popularity grabs the interest of individuals with malevolent intentions—phishing and spam email assaults. Email filtering mechanisms were developed incessantly to follow unwanted, malicious content advancement to protect the end-users. But prevailing solutions were focused on phishing email filtering and spam and whereas email labelling and analysis were not fully advanced. Thus, this study provides a solution related to email message body text automatic classification into phishing and email spam. This paper presents an Improved Fruitfly Optimization with Stacked Residual Recurrent Neural Network (IFFO-SRRNN) based on Applied Linguistics for Email Classification. The presented IFFO-SRRNN technique examines the intrinsic features of email for the identification of spam emails. At the preliminary level, the IFFO-SRRNN model follows the email pre-processing stage to make it compatible with further computation. Next, the SRRNN method can be useful in recognizing and classifying spam emails. As hyperparameters of the SRRNN model need to be effectually tuned, the IFFO algorithm can be utilized as a hyperparameter optimizer. To investigate the effectual email classification results of the IFFO-SRDL technique, a series of simulations were taken placed on public datasets, and the comparison outcomes highlight the enhancements of the IFFO-SRDL method over other recent approaches with an accuracy of 98.86%.
Hand Gesture Recognition for Disabled People Using Bayesian Optimization with Transfer Learning

Sign language recognition can be treated as one of the efficient solutions for disabled people to communicate with others. It helps them to convey the required data by the use of sign language with no issues. The latest developments in computer vision and image processing techniques can be accurately utilized for the sign recognition process by disabled people. American Sign Language (ASL) detection was challenging because of the enhancing intraclass similarity and higher complexity. This article develops a new Bayesian Optimization with Deep Learning-Driven Hand Gesture Recognition Based Sign Language Communication (BODL-HGRSLC) for Disabled People. The BODL-HGRSLC technique aims to recognize the hand gestures for disabled people's communication. The presented BODL-HGRSLC technique integrates the concepts of computer vision (CV) and DL models. In the presented BODL-HGRSLC technique, a deep convolutional neural network-based residual network (ResNet) model is applied for feature extraction. Besides, the presented BODL-HGRSLC model uses Bayesian optimization for the hyperparameter tuning process. At last, a bidirectional gated recurrent unit (BiGRU) model is exploited for the HGR procedure. A wide range of experiments was conducted to demonstrate the enhanced performance of the presented BODL-HGRSLC model. The comprehensive comparison study reported the improvements of the BODL-HGRSLC model over other DL models with maximum accuracy of 99.75%.
Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

Presently, video surveillance is commonly employed to ensure security in public places such as traffic signals, malls, railway stations, etc. A major challenge in video surveillance is the identification of anomalies that exist in it such as crimes, thefts, and so on. Besides, the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety. The recent advances of Deep Learning (DL) models have received considerable attention in different processes such as object detection, image classification, etc. In this aspect, this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and Tracking (PFPN-ADT) model for pedestrian walkways. The proposed model majorly aims to the recognition and classification of different anomalies present in the pedestrian walkway like vehicles, skaters, etc. The proposed model involves panoptic segmentation model, called Panoptic Feature Pyramid Network (PFPN) is employed for the object recognition process. For object classification, Compact Bat Algorithm (CBA) with Stacked Auto Encoder (SAE) is applied for the classification of recognized objects. For ensuring the enhanced results better anomaly detection performance of the PFPN-ADT technique, a comparison study is made using University of California San Diego (UCSD) Anomaly data and other benchmark datasets (such as Cityscapes, ADE20K, COCO), and the outcomes are compared with the Mask Recurrent Convolutional Neural Network (RCNN) and Faster Convolutional Neural Network (CNN) models. The simulation outcome demonstrated the enhanced performance of the PFPN-ADT technique over the other methods.