Reviewerly/exHarmony huggingface dataset:
Evolutionary Approaches Toward Traditional to Deep Learning-Based Chatbot

As the world is getting modern day by day, the use of artificial intelligence is also increasing exponentially. The uses of smart websites and applications are also booming. Hence, the demand of some smart assistants like chatbots is higher than ever. The concept behind chatbot includes the use of both natural language processing and ML/neural network. In this work of developing a chatbot, we first attempted to build a model, where the input sentences will be converted in a form, which can be easily fed to a ML/neural network algorithm. After this was complete, several ML algorithms were tried and tested to work behind the model, and a comparative study was done between them. Not only simple ML models, some neural network concepts like artificial neural network and feed forward neural network were also applied and discussed to determine which algorithm gave the best result with the chatbot and the reason behind it. In the end, we added a UI above everything, to make it more attractive. The details of each and every aspect are given in the paper.
DACL: A Domain-Adapted Contrastive Learning Approach to Low Resource Language Representations for Document Clustering Tasks

Clustering in Natural Language Processing (NLP) groups similar text phrases or documents together based on their semantic meaning or context into meaningful groups that can be useful in several information extraction tasks, such as topic modeling, document retrieval and text summarization. However, clustering documents in low-resource languages poses unique challenges due to limited linguistic resources and lack of carefully curated data. These challenges extend to the language modeling domain, where training Transformer-based Language Models (LM) requires large amounts of data in order to generate meaningful representations. To this end, we created two new corpora from Greek media sources and present a Transformer-based contrastive learning approach for document clustering tasks. We improve low-resource LMs using in-domain second phase pre-training (domain-adaption) and learn document representations by contrasting positive examples (i.e., similar documents) and negative examples (i.e., dissimilar documents). By maximizing the similarity between positive examples and minimizing the similarity between negative examples, our proposed approach learns meaningful representations that capture the underlying structure of the documents. Additionally, we demonstrate how combining language models that are optimized for different sequence lengths improve the performance and compare this approach against an unsupervised graph-based summarization method that generates concise and informative summaries for longer documents. By learning effective document representations, our proposed approach can significantly improve the accuracy of clustering tasks such as topic extraction, leading to an improved performance in downstream tasks.
Implementation of a steganography system based on hybrid square quaternion moment compression in IoMT

Internet of Medical Things (IoMT) systems generate medical data transmissions between patients, medical experts, and medical centers over public networks, which require high levels of security to protect the content of medical images and the personal information they contain. In this paper, we propose a new stego image encryption scheme based on a new secret image compression method, wavelet transformation, QR decomposition of the cover image, and a new chaotic map. The secret image is compressed by the Hahn-Krawtchouk hybrid quaternion square moments (HK-HQSM), which are optimized by a new hybrid metaheuristic algorithm based on the Salp Swarm Algorithm (SSA) and the Arithmetic Optimization Algorithm (AOA). To increase the security level when transmitting the proposed stego images over public networks, we introduce a new chaotic map based on the 2D fractional Henon map to encrypt the stego image. To demonstrate the effectiveness of the proposed steganography scheme for IoMT, we implemented this scheme on a low-cost Raspberry Pi 4 hardware board. The results of the performed numerical experiments show that our method is secure and provides exceptional robustness against common standard image processing attacks (steganalysis attacks). The results also demonstrate that our strategy is able to work efficiently and quickly when implemented on a Raspberry Pi board.
Ransomware: How attacker’s effort, victim characteristics and context influence ransom requested, payment and financial loss

In recent years, ransomware attacks have led to disastrous consequences for victims, not just due to the payment ransom amount but also due to the recovery costs associated with these attacks. So far only a few empirical studies have analysed the financial impact of ransomware attacks. This study aims to understand the expected financial gains for attackers and financial losses of victims after a ransomware attack. To do so, we build a dataset based on 453 ransomware attack investigation reports in the Netherlands reported to the Dutch Police between 2019 and 2022. Using rational choice model of crime (RCM) and crime scripting we hypothesise that the effort of an attacker, victim characteristics and context variables influence not only the ransom requested by an attacker but also the financial losses reported by victims. We use generalised linear models to evaluate and quantify this influence. Our results show that attacker’s efforts such as using Ransomware-as-a-Service (RaaS) and victim characteristics such as industry sector, contribute to the ransom requested by attackers and financial losses reported by victims. We also show that the availability of recoverable backups explains the likelihood of victims paying the ransom. A limitation of the present study is the interpretation of the results due to selection bias of victims willing to report to the police. Despite this limitation, we argue that our methodology and results lay the groundwork for future large-scale empirical studies and add to our understanding of attacker and victim behaviour.
The Important Influencing Factors in Machine Translation

Factored-based machine translation deals with various important linguistics information of a word during the translation process. Morphologically rich languages like Hindi provide multiple word forms from the root or dictionary word by differing in morphological information such as part of speech (POS), affixes, number, gender, etc. So, linguistic factors can provide useful information while translating morphologically rich languages. Different factors contribute in a different manner. In this chapter, we study the significance of different linguistic factors in a phrase-based statistical machine translation (SMT) framework employed for Hindi to English translation. We performed experiments over HindEnCorp and ILCI dataset for Hindi–English. We find that POS+lemma+gender achieves the highest BLEU score (16.46) for HindEnCorp dataset, and POS+lemma+number achieves the highest BLEU score (19.11) for ILCI dataset.
SwinSeg: Swin transformer and MLP hybrid network for ship segmentation in maritime surveillance system

Accurate identification and segmentation of moving ships to ensure maritime traffic safety has become an important task in maritime intelligent transportation system. With the development of artificial intelligence, maritime surveillance system based on computer vision has been widely studied. However, there are still some problems when applied to the actual maritime scene. For example, the degradation of visible image quality caused by rain, haze, and low illumination leads to a significant reduction in segmentation performance. To improve the performance of ship identification and segmentation under adverse weather conditions, we propose SwinSeg, a hybrid network combining Swin Transformer and lightweight multi-layer perceptron (MLP). To address the lack of suitable open-source datasets in the community, we have collected and labeled a semantic segmentation dataset of marine ships, named SeaShipsSeg. It consists of 1200 visible marine ship images and covers six common ship types (bulk cargo carrier, container ship, fishing boat, general cargo ship, ore carrier, and passenger ship). In addition, synthetic degraded images are added to the dataset to increase its diversity and improve the generalization ability of the network. The experimental results show that the performance of our method is significantly better than the state-of-the-art (SOTA) methods in terms of segmentation accuracy, robustness, and efficiency under different weather conditions. The dataset is available at https://github.com/GrimreaperZ-creator/SeaShipsSeg.
DPPT : A differential privacy preservation technique for cyber–physical system

Nowadays Cyber–Physical System (CPS) is implemented in several areas, such as health care, agriculture, transportation, etc. The main aim of CPS is for automation or minimization of human interaction. The conventional Machine Learning (ML) based approach involves sensitive issues such as information leakage. That issue is resolved using the Federated Learning (FL) based approach partially. The FL uses a decentralized environment for training the models, where models are trained locally, and only the updated part is sent to the data center or cloud server. It improves data privacy as the clients send only the local updates (model gradients). But also there is a high risk of data leakage while publishing the data in FL based System. A Differential Privacy Preservation Technique (DPPT) is an efficient technique to preserve information leakage. In this paper, a differential privacy preservation technique has been proposed by adding random noise to a data sample in order to generate anonymity. The performance of the model, and data quality are evaluated as privacy preservation techniques often degrade data quality.
Image steganography technique based on bald eagle search optimal pixel selection with chaotic encryption

In the digital era, information security becomes a challenging process that can be mitigated by the utilization of cryptography and steganography techniques. Earlier studies on steganography have the risk of exposing confidential data by an anonymous user. For resolving, the limitations related to the existing algorithms, one of the efficient solutions in encryption-based steganography. Encryption techniques act as an important part in protect actual data from illegal access. This study focuses on the design of Bald Eagle Search Optimal Pixel Selection with Chaotic Encryption (BESOPS-CE) based image steganography technique. The presented BESOPS-CE technique effectively hides the secret image in its encrypted version to the cover image. For accomplishing this, the BESOPS-CE technique employs a BES for optimal pixel selection (OPS) procedure. Besides, chaotic encryption was executed for encrypting the secret image, which is then embedded to choose pixel points of the cover image. Finally, embedding and extraction processes are carried out. The inclusion of the encryption process aids in accomplishing an added layer of security. A comprehensive simulation study was used to report on the BESOPS-CE approach's increased performance, and the results are examined from many angles. A thorough comparative analysis revealed that the BESOPS-CE model outperformed more contemporary methods.
Using an Artificial Intelligence Chatbot in Scientific Inquiry: Focusing on a Guided-Inquiry Activity Using Inquirybot

Abstract This study aims to explore how to use an AI chatbot pedagogically in scientific inquiry by developing a guided-inquiry activity using an AI chatbot and applying it. The developed guided-inquiry activity consisted of designing and doing inquiry activities using the transmission of sound as the topic. In this activity, a chatbot, which was given the name Inquirybot, was designed with conversation flow and scripts for guiding the inquiry design process. By applying the developed inquiry activities and Inquirybot to fifth- and sixth-grade gifted students, we found several practical possibilities and challenges for using an AI chatbot in scientific inquiry. Based on the results, future tasks and educational implications are discussed in terms of efficiently adapting AI chatbots in scientific-inquiry activities.
Estimating solar radiation using artificial neural networks: a case study of Fiche, Oromia, Ethiopia

Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom.
A federated learning algorithm using parallel-ensemble method on non-IID datasets

Abstract Traditional federated learning algorithms suffer from considerable performance reduction with non-identically and independently distributed datasets. This paper proposes a federated learning algorithm based on parallel-ensemble learning, which improves performance for image classification on these datasets. The training process of this algorithm includes basic federation learning and meta federation learning. First, several basic models are trained through cross-validation of federated learning, and then the meta-model is trained using the prediction results of the validation sets. In the training process, the training of different basic models is parallel. In prediction, meta-model is used to aggregate the output of the basic models to get the final prediction results. Our algorithm can achieve higher accuracy than traditional federated learning when using non-independent identically distributed datasets for image classification. Our algorithm aggregates different models through federated learning based on parallel-ensemble method, and improves the image classification performance of federated learning on non-independent identically distributed datasets.
VMSL: A Separation Logic for Mechanised Robust Safety of Virtual Machines Communicating above FF-A

Thin hypervisors make it possible to isolate key security components like keychains, fingerprint readers, and digital wallets from the easily-compromised operating system. To work together, virtual machines running on top of the hypervisor can make hypercalls to the hypervisor to share pages between each other in a controlled way. However, the design of such hypercall ABIs remains a delicate balancing task between conflicting needs for expressivity, performance, and security. In particular, it raises the question of what makes the specification of a hypervisor, and of its hypercall ABIs, good enough for the virtual machines. In this paper, we validate the expressivity and security of the design of the hypercall ABIs of Arm's FF-A. We formalise a substantial fragment of FF-A as a machine with a simplified ISA in which hypercalls are steps of the machine. We then develop VMSL, a novel separation logic, which we prove sound with respect to the machine execution model, and use it to reason modularly about virtual machines which communicate through the hypercall ABIs, demonstrating the hypercall ABIs' expressivity. Moreover, we use the logic to prove robust safety of communicating virtual machines, that is, the guarantee that even if some of the virtual machines are compromised and execute unknown code, they cannot break the safety properties of other virtual machines running known code. This demonstrates the intended security guarantees of the hypercall ABIs. All the results in the paper have been formalised in Coq using the Iris framework.
3D reconstruction system and multiobject local tracking algorithm designed for billiards

Abstract The use of virtual reality or augmented reality systems in billiards sports are useful tools for pure entertainment or improving the player’s skills. Depending on the purpose of these systems, tracking algorithms based on computer vision must be used. These algorithms are especially useful in systems aiming to reconstruct the trajectories followed by the balls after a strike. However, depending on the billiard modality, the problem of tracking multiple small identical objects, such as balls, is a complex task. In addition, when an amateur or nontop professional player uses low-frame-rate and low-resolution devices, problems such as blurred balls, blurred contours, or fuzzy edges, among others, arise. These effects have a negative impact on ball-tracking accuracy and reconstruction quality. Thus, this work proposes two contributions. The first contribution is a new tracking algorithm called “multiobject local tracking (MOLT)” . This algorithm can track balls with high precision and accuracy even with motion blur caused by low-resolution and low-frame-rate devices. Moreover, the proposed MOLT algorithm is compared with nine tracking methods and four different metrics, outperforming the rest of the methods in the majority of the cases and providing a robust solution. The second contribution is a whole system to track (using the MOLT algorithm) and reconstruct the movements of the balls on a billiard table in a 3D virtual world using computer vision. The proposed system covers all steps from image capture to 3D reconstruction. The 3D reconstruction results have been qualitatively evaluated by different users through a series of questionnaires, obtaining an overall score of 7.6 (out of 10), which indicates that the system is a promising and useful tool for training. Finally, both the MOLT algorithm and the reconstruction system are tested in three billiard modalities: blackball, carom billiards, and snooker.
Exploring the Potential of Mixed Reality in Enhancing Student Learning Experience and Academic Performance: An Empirical Study

In recent years, mixed reality (MR) technology has emerged as a promising tool in the field of education, offering immersive and interactive learning experiences for students. However, there is a need to comprehensively understand the impact of MR technology on students’ academic performance. This research aims to examine the effect of mixed reality technology in the educational setting and understand its role in enhancing the student’s academic performance through the student’s novel learning experiences and satisfaction with the learning environment. The present research has employed a quantitative research design to undertake the research process. The survey questionnaire based upon the five-point Likert scale was used as the data collection instrument. There were 308 respondents studying at various educational institutes in Saudi Arabia, all of whom were using mixed reality as part of their educational delivery. The findings of the present research have indicated that the application of mixed reality by creating experiential learning, interactivity and enjoyment can significantly enhance the student’s novel experience, which can directly enhance students’ satisfaction with learning objects and the learning environment, as well as indirectly enhancing the student’s academic performance. The research offers various kinds of theoretical implications and policy implications to researchers and policymakers.
Blockchain characteristics and cryptocurrency returns

We examine whether blockchain characteristics such as network size and computing power affect cryptocurrency prices and returns. Network size reflects adoption and usage of the blockchain while computing power proxies for the real-world resources expended on securing and confirming transactions. Consistent with theoretical models, we find that cryptocurrency prices exhibit comovement with these two blockchain characteristics. Further, aggregate network and computing power explain the variation in expected cryptocurrency returns at least as well as models with return-based factors such as market, size, and momentum. Overall, our results show that blockchain-based factors are important for explaining cryptocurrency prices and returns.
GTSNet: Flexible architecture under budget constraint for real-time human activity recognition from wearable sensor

Human activity recognition is an essential task for human-centered intelligent systems such as healthcare and smart vehicles, which can be accomplished by analyzing time-series signals collected from sensors in wearable devices. In these applications, real-time response is vital because prompt action is necessary for urgent events such as an elderly person falling or driving while drowsy. Although recurrent neural networks have been widely used owing to their temporal modeling capabilities, recent studies have focused on convolutional neural networks (CNNs) that are suitable for real-time responses because they incur lower computational costs. However, CNNs with a manual design may fail to achieve optimal accuracy due to varying computational budgets with applications or devices. In this paper, we propose a novel design framework that uses a mathematical approach to derive a CNN architecture suitable for a given computational budget. As a result, we introduce a grouped temporal shift network (GTSNet) with the network architecture to be flexibly modified by predefining the theoretical computation cost. We demonstrate the effectiveness of our framework in experiments, achieving the best performance for well-known public benchmark datasets under limited computational budgets. The source codes of the GTSNet are publicly available at https://github.com/jgpark92/GTSNet.
Automated Framework for Effective Identification of Oral Cancer Using Improved Convolutional Neural Network

Oral cancer is one of the most prevalent cancers worldwide, influencing and originating from the mouth and neck, and its prevalence is rising in many communities. Tobacco usage and smoking cigarettes are the leading causes of mouth cancer. A major issue continues to be the high incidence rate, delayed diagnosis, and inadequate treatment planning. Early diagnosis is crucial for a better prognosis, course of therapy, and chance of survival. Machine learning techniques have been hailed for improving diagnosis, which would subsequently reduce cancer-specific mortality and morbidity. In order to provide the effective identification of oral cancer, numerous machine learning algorithms has been used over the decade. Though machine learning algorithms are producing high level accuracy in classification task, but feature extraction is quite limited. Hence our current research utilised deep learning approach and developed the automated framework which encompasses of Pre-processing, feature extraction and classification. For effective pre-processing, contrast-limited adaptive histogram equalization (CLACHE) is used, which helps to intensify the contrast property over images and transforms the images into higher resolution. Then Gray Level Cooccurrence Matrix (GLCM) is used for feature extraction which helps to determine the features with respect to statistical texture. Finally Improved version of Convolutional Neural Network (ICNN) were employed for effective categorization of oral cancer. For analyzing the performance of developed framework, an oral cancer dataset has been used which was extracted from Kaggle Repository. Also, comparison has been made with various high performance existing approaches. As a result, our proposed framework achieves the accuracy of 97.32%, which was comparably better result than existing state-of-art approaches.