Reviewerly/exHarmony huggingface dataset:
Text Classification Using N-Grams for Providing Effective Response in Chatbot

Text classification using N-Grams can be a useful approach for providing effective responses in a chatbot. N-Grams are contiguous sequences of N words in a given text, and they can capture important patterns and contextual information. Text classification is a fundamental task in chatbot development, aiming to assign appropriate categories or labels to user input messages. One effective approach is the use of N-Grams, which are contiguous sequences of N words in a text. This paper presents a method for using various library for generating N-Grams in text classification to enhance the effectiveness of chatbot responses. Once the model is trained and evaluated, it is integrated into the chatbot. The user's input message is preprocessed, and N-Grams are extracted and encoded into numerical features. The trained classifier predicts the appropriate category or label for the user's query, enabling the chatbot to provide a pre-defined response or trigger a specific action based on the predicted label. The result shows that the utilization of N-Grams in text classification enhances the capabilities of chatbots, enabling them to provide more effective responses. By leveraging N-Grams as features and employing machine learning algorithms, chatbots can categorize user input accurately, leading to improved user experiences and interactions.
Semantic Adversarial Attacks on Face Recognition Through Significant Attributes

Abstract Face recognition systems are susceptible to adversarial attacks, where adversarial facial images are generated without awareness of the intrinsic attributes of the images in existing works. They change only a single attribute indiscriminately. To this end, we propose a new Semantic Adversarial Attack using StarGAN (SAA-StarGAN), which manipulates the facial attributes that are significant for each image. Specifically, we apply the cosine similarity or probability score to predict the most significant attributes. In the probability score method, we train the face verification model to perform an attribute prediction task to get a class probability score for each attribute. Then, we calculate the degree of change in the probability value in an image before and after altering the attribute. Therefore, we perform the prediction process and then alter either one or more of the most significant facial attributes under white-box or black-box settings. Experimental results illustrate that SAA-StarGAN outperforms transformation-based, gradient-based, stealthy-based, and patch-based attacks under impersonation and dodging attacks. Besides, our method achieves high attack success rates on various models in the black-box setting. In the end, the experiments confirm that the prediction of the most important attributes significantly impacts the success of adversarial attacks in both white-box and black-box settings and could improve the transferability of the generated adversarial examples.
Leading-edge Metaverse in Education: Framework, Applications, Challenges, and Future Development

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Development of a second‐screen system for sharing virtual reality information

Abstract Since virtual reality technology can easily realize a particular environment, it has been applied to various training. This technology is also expected in education such as those dealing with three‐dimensional figures because they can represent phenomena in three dimensions. One of the standard virtual reality technologies is wearing a head‐mounted display to experience a virtual reality project. However, the virtual reality experience is limited to the person wearing the head‐mounted display (in most cases) when considering the use of head‐mounted display‐based virtual reality in education, making its usage unsuitable for mass education. Therefore, in this study, we develop a second‐screen system that can share virtual reality information using an Android device. Healthy first‐ and second‐year high school students between the ages of 15 and 17 were asked to use the virtual reality educational materials that implemented the developed system, and a questionnaire survey was conducted. There were no negative comments in the survey on whether proposing second‐screen system was necessary, demonstrating the usefulness of the proposed system. Therefore, the second‐screen system may adapt VR technology to mass education. In addition, we developed a package to integrate our second‐screen system into the pre‐existing virtual reality projects. This package simplifies the procedure for implementing a second‐screen system, thus helping to reuse the virtual reality projects that have been developed so far.
Decentralized universally verifiable stake voting system with perfect privacy

Stake voting is a voting system where the vote cast by a voter is proportional to their account balance/asset. We propose the first implementation of an efficient, fully-decentralized, universally-verifiable stake voting system with perfect voter privacy. Our contribution is two-fold. First, we design a zero knowledge proof (ZKP) for equality of plaintexts of 2 different ciphertexts encrypted under different encryption schemes, one under ElGamal encryption scheme and another under Paillier encryption scheme. We believe that ours is the first ZKP design for this problem statement. Second, we implement a publicly-verifiable Stake voting system using the ZKP design by enforcing perfect privacy to voters and correctness of election results. Our voting system is applicable to many situations like shareholder/stockholder voting in companies. We have also provided the financial and computational breakdown feasibility of our implementation on Ganache local blockchain.
Les monuments et les <i>erga</i> de Sicile dans le corpus cicéronien

The erga and the monuments of Sicily are especially evoked in the Verrines , where they appear in the discourse to acknowledge the reality of the crime, to paint the portrait of a godless and uneducated Verres, and to establish a community of values between the Sicilians and the jury. Their presence in the rest of the Ciceronian corpus is part of the criticism of Caesar’s power: they function as political symbols.
A Time Window Analysis for Time-Critical Decision Systems with Applications on Sports Climbing

Human monitoring systems are already utilized in various fields like assisted living, healthcare or sport and fitness. They are able to support in everyday life or act as a pre-warning system. We developed a system to monitor the ascent of a sport climber. It is integrated in a belay device. This paper presents the first time series analysis regarding the fall of a climber utilizing such a system. A Convolutional Neural Network handles the feature engineering part of the sensor information as well as the classification of the task at hand. In this way, the time is implicitly considered by the network. An analysis regarding the size of the time window was carried out with a focus on exploring the respective results. The neural network models were then tested against an already-existing principle based on a mechanical mechanism. We show that the size of the time window is a decisive factor in a time critical system. Depending on the size of the window, the mechanical principle was able to outperform the neural network. Nevertheless, most of our models outperformed the basic principle and returned promising results in predicting the fall of a climber within up to 91.8 ms.
Exploring the intersections of TAM and TRI models in middle school VR technology acceptance

Background In recent years, the attitudes of middle school principals toward virtual reality (VR) have received much attention from the educational technology community. As VR continues to gain popularity in education, researchers have begun to explore middle school principals’ perceptions of VR using the Technology Acceptance Model (TAM) (perceived usefulness, and perceived ease of use) and Technology Readiness Index (TRI) (optimism, innovativeness, discomfort, and insecurity) models to explore middle school principals’ perceptions of VR. This helps to reveal the influencing factors of middle school principals’ acceptance of VR, which in turn provides theoretical support and guidance for promoting the application of VR in education. Methods To explore the factors influencing the acceptance of VR by middle school principals. We searched several databases such as Google, Scopus, and Elsevier. We focused on peer-reviewed English-language publications on VR, TAM, TRI, and middle school education from 2013 to 2023. Results Through the literature review, we found that middle school principals’ intention to use VR was significantly influenced by the TAM (perceived usefulness, perceived ease of use) and TRI (optimism, innovation, discomfort, and insecurity) models. In addition, there was some degree of intersection between the dimensions of the TRI and TAM models. Conclusion VR has been widely recognized by middle school principals as an educational tool. By providing an immersive and interactive experience, VR can be effective in improving the efficiency of school operations to a great extent.
Force-Directed Graph Layout Based on Community Discovery and Clustering Optimization

In order to visualize the important information in the knowledge graph and visualize the graph data constituting the knowledge graph for visual analysis, this paper optimizes and combines the Louvain algorithm and the force-directed graph algorithm to propose a force-directed graph layout based on community discovery and clustering optimization for the graph data. This paper uses the pruning idea to optimize the calculation steps and the community merging in the Louvain algorithm and obtains a community discovery algorithm that is more efficient and more conducive to optimizing the effect of graph layout, and introduces group elements into the force-directed graph layout to represent the community structure in graph data and implement group-based clustering optimization, so that the force-directed graph layout can clearly display the discovered community structure analyzed by the community discovery algorithm when displaying graph data, and optimize the effect and readability of the graph layout for visual analysis.
Application of Object Grasping Using Dual-Arm Autonomous Mobile Robot—Path Planning by Spline Curve and Object Recognition by YOLO—

In the trash-collection challenge of the Nakanoshima Robot Challenge, an autonomous robot must collect trash (bottles, cans, and bentos) scattered in a defined area within a time limit. A method for collecting the trash is to use machine learning to recognize the objects, move to the target location, and grasp the objects. An autonomous robot can achieve the target position and posture by rotating on the spot at the starting point, moving in a straight line, and rotating on the spot at the destination, but the rotation requires stopping and starting. To achieve faster movement, we implemented a smooth movement approach without sequential stops using a spline curve. When using the training data previously generated by the authors in their laboratory for object recognition, the robot could not correctly recognize objects in the environment of the robot competition, where strong sunlight shines through glass, because of the varying brightness and darkness. To solve this problem, we added our newly generated training data to YOLO, an image-recognition algorithm based on deep learning, and performed machine learning to achieve object recognition under various conditions.
Secure Nano-communication Framework using RSCV cryptographic circuit in IBM Q

Abstract In the cryptographic domain, quantum and its real-time hardware simulation make it easier to secure data during communication. Here, using quantum logic, a unique encryption technique called Reversible select, cross, and variation (RSCV) encryption and decryption, which involves swapping input data halves, is shown. In this article using IBM Q, we created a cryptographic encoder and decoder circuit design utilizing various quantum gates. Based on the encoder/decoder circuit, a simple nanocommunication framework is proposed. Further, to explore the application of the noise model, how to utilize this model to create noisy replicas of these quantum circuits to research the impacts of noise that occur for actual device output is shown. To reduce measurement mistakes, measurement calibration is performed using qiskit ignis model. Preparing all 2 n basis input states and calculating the likelihood of counting in the other basis states are the key concepts. The percentage improvement we achieved is 40%, 30%, and 30%, respectively, compared to earlier ones, in RSCV encryption, decryption, and RSCV cryptographic communication architecture for fake provider noise error model. It is feasible to adjust the average outcomes of an additional interesting experiment using these calibrations.
Explainable artificial intelligence for omics data: a systematic mapping study

Researchers increasingly turn to explainable artificial intelligence (XAI) to analyze omics data and gain insights into the underlying biological processes. Yet, given the interdisciplinary nature of the field, many findings have only been shared in their respective research community. An overview of XAI for omics data is needed to highlight promising approaches and help detect common issues. Toward this end, we conducted a systematic mapping study. To identify relevant literature, we queried Scopus, PubMed, Web of Science, BioRxiv, MedRxiv and arXiv. Based on keywording, we developed a coding scheme with 10 facets regarding the studies' AI methods, explainability methods and omics data. Our mapping study resulted in 405 included papers published between 2010 and 2023. The inspected papers analyze DNA-based (mostly genomic), transcriptomic, proteomic or metabolomic data by means of neural networks, tree-based methods, statistical methods and further AI methods. The preferred post-hoc explainability methods are feature relevance (n = 166) and visual explanation (n = 52), while papers using interpretable approaches often resort to the use of transparent models (n = 83) or architecture modifications (n = 72). With many research gaps still apparent for XAI for omics data, we deduced eight research directions and discuss their potential for the field. We also provide exemplary research questions for each direction. Many problems with the adoption of XAI for omics data in clinical practice are yet to be resolved. This systematic mapping study outlines extant research on the topic and provides research directions for researchers and practitioners.
Operational Level System: Transaction Processing System and Decision Making

This research paper investigates the critical role of Operational Level Systems (OLS) in facilitating transaction processing and supporting decision-making within modern organizations. In today's fast-paced and data-driven business environment, OLS plays a pivotal role in capturing, processing, and disseminating real-time data to aid in operational efficiency and effective decision-making. This study aims to explore the functionalities and interconnections of Transaction Processing Systems (TPS) as integral components of OLS, and their contribution to informed decision-making at the operational level. To achieve this, an extensive literature review is conducted to comprehend the theoretical underpinnings and practical implications of OLS and TPS. Additionally, empirical data are collected through surveys, interviews, and case studies from various industries to gain insights into the real-world implementation and benefits of OLS and its impact on decision-making processes. The findings reveal that OLS, with its robust TPS, provides organizations with streamlined data processing capabilities, enabling seamless handling of day-to-day transactions, such as sales, inventory management, and financial transactions. Moreover, it facilitates the efficient storage and retrieval of vast amounts of transactional data, laying the foundation for data-driven decision-making. Furthermore, the research highlights the significance of the integration between TPS and decision-making systems, emphasizing the pivotal role of OLS in supporting operational managers in making well-informed decisions. The ability to access real-time transactional data empowers decision-makers to identify trends, patterns, and potential bottlenecks, leading to timely adjustments and optimized performance. This research sheds light on the vital role of Operational Level Systems, particularly Transaction Processing Systems, in today's business landscape. By efficiently processing transactions and providing timely information for decision-making, OLS proves to be an indispensable tool for enhancing operational effectiveness and achieving competitive advantage. The findings of this study provide valuable insights for organizations seeking to harness the potential of OLS to streamline operations and improve decision-making processes at the operational level.
Generation of High-Quality Realistic Faces with StyleGAN

The quality of images generated using traditional GANs for face image generation is limited because the generator and discriminator share the same backpropagation network. This paper presents algorithms that aim to enhance the quality of generated images, specifically in the context of high-quality face image generation. In this paper, we use techniques like styleGAN which trump over techniques like DCGAN in face generation. We also generate a video of the morphing faces showing the full capabilities of styleGAN. This paper goes into the details of GANs, StyleGANs, and DCGANs and tries to compare the results of various GANs in doing the task above. The dataset utilized is called FFHQ.
Hand Gesture Recognition for Disabled Person Using IoT

This paper aims to address the communication barrier between individuals who have audio-vocal impairments and those who do not. Communication is typically reliant on verbal and auditory cues, making it difficult for individuals with audio-vocal impairments to engage in conversations effectively. The primary challenge stems from the limited understanding of sign language, which is commonly used by the audio-vocally impaired community. To overcome this challenge, the paper introduces a smart glove as a solution. The smart glove is designed to enable communication between disabled individuals and nondisabled individuals by utilizing finger movements. Each finger of the glove can be bent or manipulated in different ways to convey specific messages or meanings. The smart glove acts as a bridge, allowing the disabled person to express themselves through intuitive finger movements, while the non-disabled person can interpret and understand the intended message. By converting finger movements into understandable information, the smart glove facilitates real-time communication and enhances the inclusivity of conversations between individuals with audio-vocal impairments and those without. This innovative solution opens opportunities for improved social interactions, educational experiences, and professional engagements for individuals with audio-vocal impairments. It helps to break down the barriers that prevent effective communication, promoting a more inclusive society where people of all abilities can interact and understand each other more easily.
proBE–A BERTweet-Attentive Prototype Few-Shot Model for Event Detection in OSN

In the domain of digital communication, a significant transition have occurred, with social networks overtaking traditional news media outlets to become the primary source of information on a global scale. This evolution has given rise to a dynamic environment, where user-generated content escalates at an exceptional pace. However, managing this vast and ever-changing data poses significant challenges. Annotating such voluminous and dynamic information is not only financially burdensome but also impractical in terms of feasibility. In addition, current learning techniques face significant challenges when it comes to identifying unseen or novel occurrences in social network data, while also demanding a huge amount of training data to operate effectively. This entails a new era that emphasis on the need for less data and more generalisation, similar to how humans interpret information. To this end, in this research, we developed the model proBE, which formalises event detection in online social networks as a few-shot learning problem and offers a novel perspective on it. The suggested method encodes the tweet messages (aka tweets) with BERTweet, to capture context with respect to inbuilt features of Twitter like hashtags, emoticons and then employed an attentive prototype model where, tweet attention and feature attention is applied to highlight the contextually rich key tweets and the prominent features, respectively. proBE is evaluated on real world benchmark Twitter datasets CrisisLexT26 and CrisisLexT6 and performs significantly better when compared to various baseline methods in terms of accuracy, F-score, precision and recall. To the best of our knowledge, this is the first study that employs few-shot learning in event detection in online social networks to overcome the sparsity of labelled data due to the volatile dynamics of online social networks and the inability of existing approaches to detect unseen events even after enormous amount of training data.
Application of borehole cameras in the field of Engineering Geology and Hydrogeology

The use of borehole cameras in Bulgaria is increasingly used in hydrogeology and engineering geology. The resulting photographs and movies contribute to solving problems related to the study and assessment of the condition of water supply wells, as well as to obtain a clear picture on the assessment of rock massifs for their suitability for various types of construction. Borehole cameras on the territory of our country are more widely used in the field of hydrogeology compared to engineering geology. The purpose of this article is to present the possible applications of this technology, which has been improved over the years, for the study of rock massifs in their study for various infrastructure objects. Also. to shed light on the benefits of using boreholes in surveying the various water sources.
Capturing Contact Surfaces by a Frustrated Total Internal Reflection System using a Curved Plate for Analysis of the Beginning of &lt;i&gt;Humanitude&lt;/i&gt;'s Touching Motions

Analyzing time-series changes of the contact surface by touching is important to elucidate the touching skills of Humanitude as one of the pervasive multimodal comprehensive care methodologies. However, the transparent flat plates to capture contact surfaces used in the previous studies are far from the actual surfaces of care receivers because the surfaces of humans consist of curved shapes. In this paper, we propose a Frustrated Total Internal Reflection (FTIR) sensing system using a transparent curved plate for the purpose to capture contacting state by using a surface more similar to the human body. We evaluate the equivalent performance between data from the proposed FTIR sensing system and an FTIR sensing system using a transparent flat plate by calculating time-series contact areas and the quantitative indices representing theHumanitude's touching motion. Through these experiments, we conclude that the proposed system can obtain the same data quality for the contact areas and the indices as the FTIR system using the flat plate.