Reviewerly/exHarmony huggingface dataset:
Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks

This article seeks to provide a snapshot of the security of Wi-Fi access points in the metropolitan area of A Coruña. First, we discuss the options for obtaining a tool that allows the collection and storage of auditable information from Wi-Fi networks, from location to signal strength, security protocol or the list of connected clients. Subsequently, an analysis is carried out aimed at identifying password patterns in Wi-Fi networks with WEP, WPA and WPA2 security protocols. For this purpose, a password recovery tool called Hashcat was used to execute dictionary or brute force attacks, among others, with various word collections. The coverage of the access points in which passwords were decrypted is displayed on a heat map that represents various levels of signal quality depending on the signal strength. From the handshakes obtained, and by means of brute force, we will try to crack as many passwords as possible in order to create a targeted and contextualized dictionary both by geographical location and by the nature of the owner of the access point. Finally, we will propose a contextualized grammar that minimizes the size of the dictionary with respect to the most used ones and unifies the decryption capacity of the combination of all of them.
Asymmetric Training in RealnessGAN

Generative adversarial networks (GANs) have demonstrated superior performances in image generation. In recent years, various improvements of network structure and learning theory related to GANs have undergone numerous advancement. Among these improvement techniques, the asymmetric training on the generator and discriminator networks has been widely adopted. For example, the batch normalization is used in generator while the spectral normalization is used in discriminator, or using different learning rates for the generator and discriminator. However, the asymmetric training on the real and generated samples has not been taken into consideration till now. In this paper, we proposed a novel asymmetric training-based RealnessGAN (ATRGAN) which applies the idea of asymmetric training on both samples and networks. Specifically, the asymmetric training on samples refers to performing the differential learning on the real and generated samples by controlling the information entropies of real and fake anchor distributions. The asymmetric training on networks is realized via the sampling transmission <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G2D$</tex-math></inline-formula> , which abandons the commonly used independent random sampling. With the help of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$G2D$</tex-math></inline-formula> , the discriminator can obtain a dominant training position than the generator, so as to ensure that the discriminator can guide the generator more effectively during training. In addition, we proposed the floating anchor distribution technique and constructed the objective function of generator for ATRGAN. Through comparative experiments, we demonstrated ATRGAN's ability of achieving better generation performance than various SOTA GANs on CIFAR-10, CAT, and CelebA-HQ datasets.
Speech translation for Unwritten language using intermediate representation: Experiment for Viet-Muong language pair

The paper studies an automatic translation method that translates from the text of a language (L1) to the speech of an unwritten language (L2). Normally the written text is used as the bridge to connect a translation module that translates from the text of L1 to the text of L2 and a synthesis module that generates the speech of L2 from the text. In the case of unwritten language, an intermediate representation has to be used instead of the writing form of L2. This paper proposes the use of phoneme representation because of the intimate relationship between phonemes and speech in one language. The proposed method was applied to the Viet-Muong language pair. The Vietnamese text needs to be translated into Muong language in two dialects, Muong Bi - Hoa Binh and Muong Tan Son - Phu Tho, both unwritten. The paper also proposes a phoneme set for each Muong language and applies them to the problem. The evaluation results showed that the translation quality was relatively high in both dialects (for Muong Bi, the fluency score was 4.63/5.0, and the adequacy score was 4.56/5.0). The synthesized speaking quality in both dialects is acceptable (for Muong Bi, the MOS score was 4.47/5.0, and the comprehension score was 93.55%). The results also show that the applicability of the proposed system to other unwritten languages ​​is promising.
Remapping of Temperature Profile Measurements in OMNI Buoy Systems

Abstract The OMNI (Ocean Moored Buoy Network for northern Indian Ocean) buoy network comprises 12 buoy systems that measure surface meteorological parameters along with temperature and salinity profile measurements at discrete levels up to 500 m. All the OMNI buoy systems are deployed with slack-line moorings, which respond more to wind, wave, and current forcing compared to taut-line mooring. Subsurface temperature measurements are subject to change depending on both environmental condition and mooring design. The standard sensor fit of the OMNI buoy systems has only one pressure sensor fixed at 500 m, which shows significant depth variability. In order to see the spatial and seasonal variability in the vertical movement of the mooring line and the associated temperature variability, four deployments with additional pressure measurements at 200 m are analyzed. It is observed that the depth/temperature variability exhibits significant seasonality with maximum variability during pre-monsoon season. Also, the effect of this movement in the shallower depths is analyzed with four more pressure sensors in the mooring line for a 1-year period in the central Bay of Bengal. The analysis shows that the maximum value of average and root mean square (RMS) temperature deviations is 0.38 °C and 0.48 °C in the deepest interpolated depth at 400 m where the mooring line experiences a greater range of motion and the actual temperature variability in shallower depths is negligible particularly up to 75 m (&lt;0.01°C). The study reveals the necessity of additional pressure measurements for better remapping of temperature profile measurements.
The Attitudes of Technical Translators in Turkiye Towards Post-Editing

Today, the translation industry is moving towards a new dimension with the effect of technological developments. One extension of these developments is the artificial intelligence-based machine translation. This innovation, especially with the concept of post-editing, has completely changed the traditional understanding of translation and offered translators new roles such as posteditors. This study aims to examine the attitudes of technical translators in Turkey towards post-editing with an approach based on sociology of translation. In this regard, "habitus" and "interest", the key concepts of French sociologist Pierre Bourdieu, constitute the theoretical framework of the study. The reason technical translators are chosen as the research object is that machine translation offers very successful outputs in technical translation. The study concludes that technical translators in Turkey have a positive attitude towards post-editing as they are satisfied with the economic gains they have obtained from the field and have succeeded in adapting to the changing system with their habitus suitable for the needs of the field. Data collection was carried out using a questionnaire consisting of 13 open-ended questions. Since the study was conducted with a group of 10 participants from 3 different companies, additional empirical studies are required to support the findings.
An Improved YOLO Model for Vehicle Recognition System in Aerial Imagery

The modern development in unmanned aerial vehicles (UAV) providing aerial imagery attracts researchers to improve the object detection algorithms to be used in various applications. Lightweight object detection models are required for low computational resource devices. This study developed a lightweight object detection model by improving the architecture of YOLOv4 Tiny to detect vehicles from the VEDAI dataset. In the developed model, one additional scale feature map is added to the architecture. Besides that, the sizes of output images for the second and third prediction boxes are upscaled with the aim of detecting the small pixels of vehicles in the aerial imagery with better accuracy. The experimental results showed an improvement in the detection accuracy and precision when compared with several state-of-the-art methods to detect small objects such as vehicles in aerial imagery.
Electronic Voting System Essentials and Problems

The development of reliable and safe e-voting systems is relevant because of the wide range of applications. This paper provides an analysis of modern electronic voting systems based on security criteria. An analysis was conducted based on the most popular modern e-voting system architectures. The analysis provides a baseline for developing a secure e-voting system.
A New Image Decolorization Evaluation Quality Metric

Image decolorization, the process of color-to-gray conversion, plays a crucial role in single-channel processing, computer vision, digital printing, and monochrome visualization. This process induces new artifacts, the impact of which on visual quality has to be identified. While visual quality assessment has been the subject of many studies, there are still some open questions regarding new color-to-gray conversion quality metrics. For example, computer simulations show that the commonly used grayscale conversion quality metrics such as CCPR, CCFR, and E-score depend on parameters and may pick different best decolorization methods by changing the parameters. This paper proposes a new quality metric to evaluate image decolorization methods. It uses the human visual properties information and regression method. Experimental results also show (i) strong correlations between the presented image decolorization quality metric and the Mean Opinion Score (MOS), (ii) more robust than the existing quality metrics, and (iii) help to choose the best state-of-the-art decolorization methods using the presented metric and existing quality metrics.
Dynamic Force-directed Graph with Weighted Nodes for Scholar Network Visualization

Through the growth of portals and venues to publish academic publications, the number of academic publications is growing exponentially in recent years.An effective exploration and fast navigation in the collection of academic publications become an urgent need to help academic researchers find publications related to their research and the surrounding community.A scholar network visualization approach is proposed to help users to explore a large number of academic publications concerning the strength of the relationship between each publication.The approach is realized by creating a web-based interface using D3 JavaScript algorithm that allows the visualization to focus on how data are connected to each other more accurately than the conventional lines of data seen in traditional data representation.The proposed approach visualizes data by incorporating a force-directed graph with weighted nodes and vertices to give more descriptive information of millions of raw data such as author names, publication title, publication year, publication venue and number of citations from the scholar network dataset.By introducing a weighted relationship in the network visualization, the proposed approach can give a more insightful detail of each publication such as a highly cited publication by looking at and exploring the generated interactive graph.The proposal is targeted to be incorporated into a larger-scale scholar network analytical dashboard that can offer various visualization approaches under one flagship application.
Dynamic Time Warping Features Extraction Design for Quranic Syllable-based Harakaat Assessment

The use of technological speech recognition systems with a variety of approaches and techniques has grown exponentially in varieties of human-machine interaction applications.The assessment for Qur'anic recitation errors based on syllables utterance is used to meet the Tajweed rules which generally consist of Harakaat (prolonging).The digital transformation of Quranic voice signals with identification of Tajweed-based recitation errors of Harakaat is the main research work in this paper.The study focused on speech processing implemented using the representation of Quranic Recitation Speech Signals (QRSS) in the best digital format based on Al-Quran syllables and feature extraction design to reveal similarities or differences in recitation (based on Al-Quran syllables) between experts and student.The method of Dynamic Time Warping (DTW) is used as Short Time Frequency Transform (STFT) of QRSS syllable feature for Harakaat measurement.Findings from this paper include an approach based on human-guidance threshold classification that is used specifically to evaluate Harakaat based on the syllables of the Qur'an.The threshold classification performance obtained for Harakaat is above 80% in the training and testing stages.The results of the analysis at the end of the experiment have concluded that the threshold classification method for Minimum Path Cost (MPC) feature parameters can be used as an important feature to evaluate the rules of Tajwid Harakaat embedded in syllables.
Upcoming meetings

Artificial OrgansVolume 47, Issue 1 p. 228-228 UPCOMING MEETINGS Upcoming meetings First published: 31 December 2022 https://doi.org/10.1111/aor.14464Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume47, Issue1January 2023Pages 228-228 RelatedInformation
An Intelligent Handwritten Digits and Characters Recognition System

The process of giving machines the ability to recognize human handwritten digits and characters is known as handwritten digit and character recognition. Handwritten digits and characters are imperfect, vary from person to person, and can be constructed with a variety of flavors. Therefore, it's not a simple assignment for the machine. In this paper, a machine learning algorithm has been made to detect handwritten digits and characters with high accuracy relative to the past models. The MNIST dataset is used to provide the model with the training and test datasets for its variety of data. Another dataset is Kaggle's A-Z Handwritten Alphabets dataset, which is used for the English letter’s dataset. It contains a total of 370,000 images of English letters. The paper's output is a Graphical user interface (GUI) that allows users to draw a digit or character and instantly see a digital version of it along with an accuracy %. The convolutional neural network (CNN) approach was applied as a deep learning method. The suggested CNN model is based on the Keras model, which classifies handwritten digit pictures using an RMSprop optimizer. With epoch 10, the suggested CNN model achieves 98.80 percent accuracy during testing and 99.06 percent accuracy during training. Average macro accuracy of 0.99 was attained. It has a 0.98 average macro recall. The macro average F1 score reached 0.99.
Observer-Based AILC Design for Robotic Manipulator

In this chapter, the research for plants with unmeasurable states and unknown control gain is carried out by taking manipulator as investigation object, which successfully overcomes the design difficulty from unknown control gain, absence of measurement of states and output delays. During the design the observer gain is determined by using LMI method and hyperbolic tangent function and convergent sequence are employed to design the robust term for purposed of guaranteeing the learning convergence.
Parameter Estimation for Quantum Trajectories: Convergence Result

Quantum trajectories describe the evolution of quantum systems undergoing indirect measurement. In the discrete-time setting, the state of the system is updated by applying Kraus operators according to the measurement results. From an experimental perspective, these Kraus operators can depend on unknown physical parameters p. An interesting and powerful method has been proposed in [1] to estimate a parameter in a finite set; however, complete results of convergence were lacking. This article fills this gap by rigorously showing the consistency of the method, whereas there was only numerical evidence so far. When the parameter belongs to a continuous set, we propose an algorithm to approach its value and show simulation results.
AE-GAN: Attention Embedded GAN for Irregular and Large-Area Mask Face Image Inpainting

Existing image inpainting methods have shown promising results for regular and small-area breaks. However, restoration of irregular and large-area damage is still tricky and achieves mediocre results due to the lack of restrictions on the center of the hole. In contrast, face inpainting is also problematic due to facial structure and texture complexity, which always results in structural confusion and texture blurring. We propose an attention embedded adversarial generative network (AE-GAN) in the paper to solve this problem. Overall our framework is a U-shape GAN model. To enable the network to capture the practical features faster to reconstruct the content of the masked region in the face image, we also embed the attention mechanism that simplifies the Squeeze-and-Excitation channel attention mechanism and then set it reasonably in our generator. Our generator is chosen the U-net structure as a backbone. Because the structure can encode information from low-level pixels context features to high-level semantic features. And it can decode the features back into an image. Experiments on CelebA-HQ datasets demonstrate that our proposed method generates higher quality inpainting, results in consistent and harmonious facial structures and appearance than existing methods, and achieves state-of-the-art performance.
Analyses on selected techniques for crime detecting among Pupils of Police Model Primary School, Wudil Kano

Crime is an epidemic, and there is a need to spot crime doers. The research aimed to analyze systemic technical approaches to nipping crime in the bud. Police Primary School was employed. Recruitment was 100 pupils. They were five classes in all, and each classroom had 20 pupils. The pupils stayed in their classrooms. The research is in two phases: Phase 1 is voice recognition, and phase 2 is the truth serum approach. Phase 1 was to test the ability of students to recognize each other through a voice from spoken words between close associates. Each class was allocated twenty seconds for the ear witness voice recognition. The pupils in each class block utter sounds (speak), one after the other, and the other class block keenly listens to their voices for identification. In phase 2, pupils receive items as an enticement for the truth. This item is mind-altering, and the crime's identity is known. The research analyzed information obtained from the pupils after inducement to ascertain the doer of the crime. In phase 1, pupils in the same classrooms' voice recognition scored above 70%, while pupils in different classrooms scored above 60% in all the classrooms demonstrated. Only Classrooms B and D were non-significant from other classrooms (P&gt;0.05) in the same classrooms, while non-significant differences in the different classroom settings were observed. Pase-2 revealed above 60% of the crime doers. Only two classrooms were not significant. The most frequent crime is noise making. The divulging of information on crime doers was comparable in all. Keywords: Crimes, Pupils, voice recognition, truth serum, classrooms and Police Model School.
Transformer-based Cross-Lingual Summarization using Multilingual Word Embeddings for English - Bahasa Indonesia

Cross-lingual summarization (CLS) is a process of generating a summary in the target language from a source document in another language.CLS is a challenging task because it involves two different languages.Traditionally, CLS is carried out in a pipeline scheme that involves two steps: summarization and translation.This approach has a problem, it introduces error propagation.To address this problem, we present a novel end-toend abstractive CLS without the explicit use of machine translation.The CLS architecture is based on Transformer which is proven to be able to perform text generation well.The CLS model is a jointly trained CLS task and monolingual summarization (MS) task.This is accomplished by adding a second decoder to handle the MS task, while the first decoder handles the CLS task.We also incorporated multilingual word embeddings (MWE) components into the architecture to further improve the performance of the CLS models.Both English and Bahasa Indonesia are represented by MWE whose embeddings have already been mapped into the same vector space.MWE helps to better map the relation between input and output that use different languages.Experiments show that the proposed model achieves improvement up to +0.2981 ROUGE-1, +0.2084 ROUGE-2, and +0.2771 ROUGE-L when compared to the pipeline baselines and up to +0.1288 ROUGE-1, +0.1185 ROUGE-2, and +0.1413 ROUGE-L when compared to the endto-end baselines.
Application of the Elimination Competition Mechanism Based on Blockchain Multi-supervision in Vehicle Data Sharing

With the rapid development of the Internet of Vehicles and the advent of the 5G era, it is essential to share traffic information among vehicles quickly, securely, and efficiently. Data sharing among vehicles can not only improve driving safety but also alleviate traffic congestion and enhance traffic efficiency. Although existing hybrid blockchain-based vehicle information sharing schemes have improved the transmission speed of vehicle information sharing from various perspectives, there are still challenges in the security of data transmission and transaction settlement rate. In this paper, we propose a vehicle data sharing model based on blockchain multi-supervision, in which the vehicle data and transaction records will be packaged and uploaded by the Road Side Unit (RSU) to the blockchain supervisory committee for review, thus improving the security of the scheme. The experimental results show that this scheme can achieve fast updates and secure storage of vehicle data. At the same time, the built hierarchical strategy can effectively reduce the load and system overhead of the blockchain, thus meeting the security and performance requirements of the current Internet of Vehicles application scenario.