Reviewerly/exHarmony huggingface dataset:
Estimation of Hazardous Environments Through Speech and Ambient Noise Analysis

In recent years, significant attention has been di-rected towards the development of artificial empathy within the engineering academic community. Replicating artificial empathy necessitates the capability of agents to discern human emotions and comprehend environmental risks. Analyzing acoustic data in real environments offers a higher level of non-invasive pri-vacy compared to video and camera data, limiting the agent’s understanding to specific patterns. However, current studies are negatively affected by subjective inferences from real data, which can result in inaccurate predictions, leading to both false positives and negatives, especially when contextual data and human speech are involved. This paper work proposes the estimation of a dangerous environment in accordance with the emotional speech and additional ambient noises. In this approach we implement a variational autoencoder model in conjunction with a classifier for training the classification task. Additional regularization techniques are applied to bridge the gap between the original training data and the expected data. The classifier utilizes feature data generated by the variational autoencoder to extract class patterns and determine whether the environment is hazardous. Emotional speech is classified as angry, sad, or scared emotions, contributing to the classification of danger, while happy, calm, and neutral emotions are considered safe. Various ambient noise types, including gunfire and broken glass, are categorized as dangerous, while real-life indoor noises like cooking, eating, and movements are considered safe.
Automatic Bangla Image Captioning Based on Transformer Model in Deep Learning

Indeed, Image Captioning has become a crucial aspect of contemporary artificial intelligence because it has tackled two crucial parts of the AI field: Computer Vision and Natural Language Processing. Currently, Bangla stands as the seventh most widely spoken language globally. Due to this, image captioning has gained recognition for its significant research accomplishments. Many established datasets are found in English but no standard datasets in Bangla. For our research, we have used the BAN-Cap dataset which contains 8091 images with 40455 sentences. Many effective encoder-decoder and Visual Attention approaches are used for image captioning where CNN is utilized for the encoder and RNN is used for the decoder. However, we suggested a transformer-based image captioning model in this study with different pre-train image feature extraction models like Resnet50, InceptionV3, and VGG16 using the BAN-Cap dataset and find out its effective efficiency and accuracy based on many performances measured methods like BLEU, METEOR, ROUGE, CIDEr and also find out the drawbacks of others model.
Research on Automatic Classification of Writing Styles Based on KNN Algorithm

Novels written by different writers have different literary characteristics, which are manifested in various aspects such as vocabulary, sentence structure, rhetorical techniques, and others. Classifying writing types and sentence characteristics helps identify writers. In the vector space modeling method, sentence classificationification was taken as the feature, and the mixed sentence analysis method was used to reduce the dimension of the sentence classificationification vector. The ITC algorithm weighed the attribute items, and the KNN algorithm was used to sort. Integrated judgment was made according to the writing style classifier. The function of the classifier turned out to have the potential for the improvement of sorting and identifying literary styles of Chinese modern and contemporary novelists.
Feasibility Study of Using Augmented Mirrors for Alignment Task during Orthopaedic Procedures in Mixed Reality

Accurate depth estimation poses a significant challenge in egocentric Augmented Reality (AR), particularly for precision-dependent tasks in the medical field, such as needle or tool insertions during percutaneous procedures. Augmented Mirrors (AMs) provide a unique solution to this problem by offering additional non-egocentric view-points that enhance spatial understanding of an AR scene. Despite the perceptual advantages of using AMs, their practical utility has yet to be thoroughly tested. In this work, we present results from a pilot study involving five participants tasked with simulating epidural injection procedures in an AR environment, both with and without the aid of an AM. Our findings indicate that using AM contributes to reducing mental effort while improving alignment accuracy. These results highlight the potential of AM as a powerful tool for AR-enabled medical procedures, setting the stage for future exploration involving medical professionals.
Spatial Structure-oriented and Angle-based Human Pose Estimation for Pose Classification

This study provides a detailed analysis of the performance of different pose classification models trained using data from the human pose classification model. The approach involves considering both spatial structure-oriented techniques, which incorporate body part coordinates and their relative positions, and angle-based methods that calculate the angles between joints. This combined spatial and angular data play a crucial role in enhancing the precision of pose classification. It is worth noting that while our primary investigation is based on a yoga pose dataset, the versatility and applicability of our approach extend to other pose datasets, showcasing the broad potential of our spatial and angle-based methodology. In summary, this research embarks on the integration of Human Pose Estimation with machine learning for yoga pose classification. The outcomes promise not only to advance the field of pose classification but also to yield practical applications in exercise, fitness, and beyond. This research has practical implications, aiming to integrate the developed model into a project we developed titled “AI-Based Human Pose Detection Tool. “The tool uses real-time video analysis to track users' movements during workouts, with the Blazepose model detecting key landmarks and assessing metrics. This enhances posture and form assessment, making the tool valuable for fitness enthusiasts.
Exploring Stakeholders’Perceptions and Experiences at the 10-Year Mark of the Open Recruitment System

This study conducted focus group interviews with principals, teachers, and parents who participated in each type of principal search to understand their perceptions and experiences with the principal recruitment system process which began in 2012. As a result, the following three major themes and 11 sub themes were identified: first, before the start of the principal recruitment system, secondly, “lights and shadows” of the principal recruitment system operating schools, and, lastly, remaining challenges and suggestions. The results showed that the principal recruitment system was mainly initiated as a breakthrough to solve school change needs and problems, and in particular, stable customized school operation and active educational activities were mentioned as the biggest strengths. However, the perception of “hard school with a lot of work” and the negative view of the principal recruitment system still remain. Based on the current status of the principal search system, remaining challenges and suggestions were discussed.
Damage GAN: A Generative Model for Imbalanced Data

This study delves into the application of Generative Adversarial Networks (GANs) within the context of imbalanced datasets. Our primary aim is to enhance the performance and stability of GANs in such datasets. In pursuit of this objective, we introduce a novel network architecture known as Damage GAN, building upon the ContraD GAN framework which seamlessly integrates GANs and contrastive learning. Through the utilization of contrastive learning, the discriminator is trained to develop an unsupervised representation capable of distinguishing all provided samples. Our approach draws inspiration from the straightforward framework for contrastive learning of visual representations (SimCLR), leading to the formulation of a distinctive loss function. We also explore the implementation of self-damaging contrastive learning (SDCLR) to further enhance the optimization of the ContraD GAN model. Comparative evaluations against baseline models including the deep convolutional GAN (DCGAN) and ContraD GAN demonstrate the evident superiority of our proposed model, Damage GAN, in terms of generated image distribution, model stability, and image quality when applied to imbalanced datasets.
Hong–Ou–Mandel interference of spin–orbit hybrid entangled photons

Structured photons are a crucial resource in both classical and quantum technologies, particularly in spin–orbit hybrid photons, enabling various practical applications ranging from ultra-sensitive metrology techniques to quantum-enhanced information processing tasks. However, the two-photon interference of spin–orbit hybrid photons, which combines polarization modes and complex transverse spatial structures across the beam profile, remains unexplored. Here, we present an experimental observation of Hong–Ou–Mandel (HOM) interference of spin–orbit hybrid photons. The tunable q-plates that work as spin–orbit coupler devices are used to prepare various forms of spin–orbit hybrid entangled photons. By harnessing the match degree in the temporal domain, the coalescence and anti-coalescence effects resulting from the symmetric and anti-symmetric properties of the incident quantum states are observed. Moreover, we demonstrated the feasibility of quantum-enhanced photon polarization gears through HOM interference and theoretically analyze the noise-resilient advantages based on coherent HOM measurements. These results provide an alternative route toward quantum experiments with structured photons that allows for controlling their quantum interference in a compact, stable, and efficient way.
The Promotion of Hate Speech: From a Media and Journalism Perspective

ABSTRACTThis work presents the key ideas of the 13 works that make up the special issue, “The Role of News Media in Promoting Hate Speech,” exploring the mechanisms by which hate speech spreads in media-related environments. The objective of this collection of studies is. The authors of these works, hailing from Spain, the United States, India, Portugal, and Norway, have contributed to establishing an approach to the central issue from diverse perspectives, including a legal perspective and addressing elements associated with journalistic practice and the dissemination of hate speech in current digital communication scenarios and help to exacerbate the polarization and prejudice in the public opinion of our current societies.KEYWORDS: Hate speechsocial mediajournalistdigital mediamass mediaprofessional practicesocial conflicts Disclosure StatementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Hatemedia's project PID2020-114584GB-I00, financed by MCIN/ AEI /10.13039/501100011033
Development of E-Module Based on Augmented Reality Integrated with Ethno-STEAM for Carving Studies at MI Hasyim Asy'ari Bangsri

The problem faced at MI Hasyim Asy'ari is the lack of use of technology in learning and introducing local wisdom. This research aimed to examine the Properness of an Augmented Reality-based E-Module integrated with Ethno-STEAM for carving studies at MI Hasyim Asy'ari Bangsri. This research used the development method (R&amp;D). The development procedure in this research was a 4D model including Define, Design, Develop and Disseminate. The research trial sample was carried out on a small scale, namely 20 class VI students, and on a large scale, namely 40 class V students at MI Hasyim Asy'ari Bangsri. Data collection techniques used interviews with class V teachers and carving craftsmen in Jepara, documentation carried out by taking photos during research, and questionnaires given to material experts, media experts, language experts, test instrument experts, questionnaire responses from class V art teachers, and student response questionnaire. Data analysis used Properness analysis with the Aiken's V test. The analysis results for material experts were 98% in the very valid category, media experts were 98% in the very valid category, linguists were 97 in the very valid category, first test instrument experts were 94 in the category very valid and the expert on the second test instrument was 97% in the very valid category, the class V art teacher response questionnaire was 97% in the very valid category, and the student response questionnaire was 79.5% in the good category. The overall results of the Augmented Reality-based E-Module Properness trial using the Aiken's V method were 0.84375 with a high validity category. So that the E-Module, which has been tested on class VI students, is declared suitable for use by elementary school students. Keywords: E-Modul, Augmented Reality, Ethno-STEAM, Carve
Unravelling the Relationship between Governance Indicators and Foreign Direct Investment in Tanzania: An Empirical Analysis

Foreign Direct investment (FDI) is recognized as a significant source of capital inflows that can stimulate economic growth in developing countries. In order to attract and benefit from FDI, governments have implemented various economic reforms and focused on improving governance indicators. This study investigated the causality effect of governance indicators on FDI inflows in Tanzania. The governance indicators examined in this study include; rule of law, regulatory quality, government effectiveness, control of corruption, voice and Accountability, political stability and absence of violence. Before measuring the causality effect between the variables, a stationarity, test was conducted using the Augmented Dickey-Fuller (ADF) test. Then the Granger Causality Test was used to address two-way linkages between variables. The data used in the study was secondary quantitative time series data obtained from the World Bank Worldwide Governance Indicators and the Bank of Tanzania from 1996 to 2021. The findings suggested a long-run causality running from governance indicators to FDI inflows in Tanzania. In the short run, voice and accountability, political stability and absence of violence, government effectiveness, regulatory quality, rule of law, and control of corruption individually influence FDI inflows. Furthermore, the Granger causality test indicates that voice and accountability and political stability and absence of violence Granger cause FDI inflows. The other governance indicators also exhibited significant causality with FDI inflows. These results call for policy makers in Tanzania to focus on strengthening governance framework, ensure accountability, enhance investor protection, ensure political stability, promote the rule of law and prevent corruption in achieving increased FDI for sustained economic growth.
PEMANFAATAN TEKNOLOGI INFORMASI UNTUK MENINGKATKAN PENJUALAN DAN PEMASARAN PRODUK OLAHAN SAYUR KELOMPOK TANI DESA SAMIRAN, KECAMATAN SELO, KABUPATEN BOYOLALI

Abstract&#x0D; The innovation produced by the community of Samiran Village, Selo, Boyolali Regency by processing vegetables into vegetable chips provides high economic opportunities for the community. Vegetable chips produced by farmer groups are currently only sold in the Boyolali area. So that vegetable chips can be known more widely and generate higher income, the author is motivated to carry out community service activities. This activity was carried out with the aim of motivating farmer groups to be able to sell and market using social media. In addition, this activity provides knowledge to farmer groups on how to use social media so that they can increase sales and do marketing properly. The implementation method used in this activity is a workshop on the importance of using social media and how to use social media to support marketing and product sales. The activity was carried out smoothly. All participants participated in the workshop activities by actively asking questions and following all directions and procedures for making social media well. Suggestions for further community service activities are to conduct periodic reviews in order to monitor the promotion and sales process of vegetable chips and provide additional material related to how to create content that can attract consumers to buy vegetable chips.&#x0D; Keywords: Information Technology, Sales, Marketing, Processed Vegetable, Samiran Village
Radar Instance Transformer: Reliable Moving Instance Segmentation in Sparse Radar Point Clouds

The perception of moving objects is crucial for autonomous robots performing collision avoidance in dynamic environments. LiDARs and cameras tremendously enhance scene interpretation but do not provide direct motion information and face limitations under adverse weather. Radar sensors overcome these limitations and provide Doppler velocities, delivering direct information on dynamic objects. In this paper, we address the problem of moving instance segmentation in radar point clouds to enhance scene interpretation for safety-critical tasks. Our Radar Instance Transformer enriches the current radar scan with temporal information without passing aggregated scans through a neural network. We propose a full-resolution backbone to prevent information loss in sparse point cloud processing. Our instance transformer head incorporates essential information to enhance segmentation but also enables reliable, class-agnostic instance assignments. In sum, our approach shows superior performance on the new moving instance segmentation benchmarks, including diverse environments, and provides model-agnostic modules to enhance scene interpretation. The benchmark is based on the RadarScenes dataset and is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://doi.org/10.5281/zenodo.10203864</uri> .
HACR-MDL: HANDWRITTEN ARABIC CHARACTER RECOGNITION MODEL USING DEEP LEARNING

Abstract. Despite the enormous effort and prior research, Arabic handwritten character recognition still has a deep, wide-ranging, and untapped scope for study owing to the enormous challenges faced in this research area. The reason for such challenges is that the Arabic script comprises 28 alphabets, each of which can be written in two to four different forms depending on where it appears in a word—beginning, middle, end, or isolated. The Convolutional Neural Network (CNN or ConvNet) is a subtype of neural network that is commonly used in image classification, speech recognition, video processing, object detection, and segmentation because its built-in convolutional layer reduces the high dimensionality of images without losing significant information. Hence, the scope of this study is to examine the classification performance of various deep CNN models on offline handwritten Arabic character recognition. Based on the experimental comparative studies, this research proposes a Handwritten Arabic Character Recognition Model using Deep Learning (HACR-MDL), a modified CNN model. The proposed model is trained and tested using the AHCD dataset achieving an accuracy of 98.54%. The results achieved showed that HACR outperformed the recent research offline handwritten Arabic character recognition in terms of model complexity, speed, model parameters, and performance metrics.
Robust Color Image Encryption Scheme Based on RSA via DCT by Using an Advanced Logic Design Approach

Information security in data storage and transmission is increasingly important. On the other hand, images are used in many procedures. Therefore, preventing unauthorized access to image data is crucial by encrypting images to protect sensitive data or privacy. The methods and algorithms for masking or encoding images vary from simple spatial-domain methods to frequency-domain methods, which are the most complex and reliable. In this paper, a new cryptographic system based on the random key generator hybridization methodology by taking advantage of the properties of Discrete Cosine Transform (DCT) to generate an indefinite set of random keys and taking advantage of the low-frequency region coefficients after the DCT stage to pass them to a subsystem consisting of an Reversible Logic Gate (RLG) group to obtain the secret keys that are passed to Rivest Shamir Adleman (RSA) to finish encrypting the image. The results indicate that the proposed method has the ability to generate a very large set of highly complex and secure secret keys that can be used later in the encryption stage. Moreover, the number and complexity of those keys will change each time the image is changed, and this represents the contribution of the proposed method. They experienced no time loss throughout the encryption and decryption processes when using RLG, which indicates that the proposed system did a good job in making different keys from the same image. And it differs in the strength of the key from one image to another, depending on the nature of the color imge.
A Conceptual Cloud Forensic Investigation Process Model for Software as a Service(SaaS) Applications

This paper explore a structured and systematic approach cloud forensic investigation process model for SaaS applications, to investigate the digital crimes in the cloud environment and contributing to enhanced security and privacy of acquired data during forensic investigation .The proposed model offers the distinctive characteristics of cloud environments and the varying levels of access and control within them. In this proposed model, the systematic forensic investigation process is detailed with microscopic details with four phases namely the initial phase, the acquisition phase, the analysis phase, and the reporting phase in Cloud environment. Ultimately, this research aims to enhance the overall trustworthiness and reliability of SaaS applications forensic for fostering a safer and more secure cloud computing forensic investigation landscape by using the chain of custody.
A New Way for Face Sketch Construction and Detection Using Deep CNN

Traditional hand-drawn face sketches have encountered speed and accuracy issues in the field of forensic science when used in conjunction with contemporary criminal identification technologies. To close this gap, we provide a ground-breaking research article that is built on a stand-alone program that aims to revolutionize the production and identification of composite face sketches. This ground-breaking approach does away with the requirement for forensic artists by enabling users to easily create composite sketches using a drag-and-drop interface. Utilizing the power of deep learning and cloud infrastructure, these generated sketches are seamlessly cross-referenced against an enormous police database to identify suspects quickly and precisely. Our research study offers a dual-pronged approach to combating the rise in criminal activity while using the quick breakthroughs in artificial intelligence. First, we demonstrate how a specific Deep Convolutional Neural Network model transforms sketches of faces into photorealistic photographs. Second, we employ transfer learning for precise suspect identification using the pre-trained VGG-Face model. Utilizing Convolutional Neural Networks, which are famous for their data processing powers and hierarchical feature extraction, is a key component of our strategy. This approach exceeds current methods and boasts an extraordinary average accuracy of 0.98 in identifying people from sketches, providing a crucial tool for strengthening and speeding up forensic investigations. A unique Convolutional Neural Network framework that demonstrates significant improvements over state-of-the-art techniques is also revealed as we dive into the challenging task of matching composite sketches with corresponding digital photos. Our thorough analysis shows the framework to be remarkably accurate, constituting a substantial advance in the field of forensic face sketch production and recognition.