Reviewerly/exHarmony huggingface dataset:
MixTBN: A Fully Test-Time Adaptation Method for Visual Reinforcement Learning on Robotic Manipulation

Learning adaptive policies that can transfer to unseen environments remains challenging in visual generalizable learning (RL). Existing methods commonly learn a robust policy via data augmentation and domain randomization for better generalization. Limited by the unobservability of the target environment, these methods are unable to utilize reward signals for model fine-tune and adaptatively transfer into new scenarios. In this work, we first investigate how a visual RL agent would benefit from the Test-time Adaptation. Surprisingly, we find that the optimization on the Batch-Normalization layer could significantly improve the generalization of visual RL. Hence, we propose a lightweight test-time adaptation algorithm Mix Test-time Batch Normalization (MixTBN), which adaptively transfer the learnt policy into unseen environments without any additional parameter. By solely mixing the statistics of the Batch Normalization layers, our method achieves a state-of-the-art performance on two robotic manipulation tasks. Extensive ablation experiments demonstrate the effectiveness of each component of our method.
Traffic Sign Detection and Recognition under Foggy Conditions

As one of the key technologies of autonomous driving, traffic sign detection and recognition (TSDR) is still a challenging task due to the difficulty and complexity of traffic signs. In addition, the real world often has bad weather conditions, which makes detection more difficult. In this paper, we propose a multi-source traffic sign dataset for foggy scenes, named Foggy-DFG, which contains a large number of street view data for foggy conditions. A YOLOX-CBAM model with attention module is proposed. The attention module improves the performance of YOLOX by fusing and emphasizing the attention of specific locations to emphasize traffic signs under foggy conditions. Experiments on Foggy Driving dataset shows that the proposed method has achieved significant improvement.
Popularity Inference Based on Semantic Sentiment Analysis of YouTube Video Comments

YouTube is a popular online video-sharing and social media platform, hosting diverse content for all ages. The platform is gradually gaining ground against traditional media, with major turnover increasingly turning there due to videos and creators' influence, especially in younger audiences. Some of the videos may attain a large number of views often accompanied by many comments. Comments, often focus not only on the content of the video but also on several other aspects and topics. In this paper, we focus on the sentiment analysis of video comments, using a variety of methods we explore the correlation between user comments' sentiment polarity score and the popularity of the relevant video. Some adversities of machine learning or neural network model approach to the task include linguistic irregularities, typing errors, niche vocabulary, sarcastic and ambiguous content, or unrelated events influencing the commented themes. Unlike previous works [1], [2], our focus is centered on leveraging comment sentiment polarity for deriving predictions of the popularity and success of the respective videos. Our methods depend on lexicon-based sentiment analysis approaches like VADER and TextBlob but also on neural network architectures including BERT and RoBERTa. Our experimental analysis resulted in promising results, setting a baseline for this application theme.
A Double Regularization Loss Based on Long-tailed Noisy Labels

Extensive research has been conducted in recent years to solve the long-tailed distribution and achieved excellent results. However, in contrast to well-designed data, datasets with label noise are common in the real world, even in long-tailed datasets. Then, loss functions that rely on prior knowledge of correct labels for long-tailed distributions will fail. To solve the above problems, the robustness of different loss functions in long-tailed data containing noise is first analyzed. Then algorithmic improvements are made to the LDAM loss, which is focused on dealing with long-tailed problems. We propose a robust loss function DRL, which solves the noisy long-tailed problem with two regularisation terms: label regularisation and sample regularisation. Experiments on several datasets validate the effectiveness of the proposed loss function.
A Clustering-Based Approach for Ranking Universities

In recent years, there has been an increasing interest among international organizations and academic communities in the evaluation and ranking of educational institutions. Such rankings are of great significance to various stakeholders, including students and their families, academic staff, funding agencies, and universities themselves. Ranking methodologies typically involve the use of diverse indicator categories, aimed at ensuring objec-tivity and reflecting the relative importance of each university. These categories may include, but are not limited to, research output and impact, teaching quality and learning environment, international diversity, and reputation in the employment market. Apart from conventional ranking methods, clustering-based techniques can also be used to group universities according to their common characteristics and to evaluate their performance within each cluster. In this paper, we seek to explore how universities from various regions can be clustered into distinct groups and ranked based on their unique features. For this purpose, we employ various clustering algorithms, each possessing distinct characteristics, and we present the similarities between them to obtain the most effective evaluation. We also analyze how university features can impact the evaluation and classification process into a cluster. We present some statistical results of university rankings and finally, we visualize the clustering process to facilitate a deeper understanding of the findings.
A Conversational Digital Assistant for STEM Education

The rapid advancement of artificial intelligence offers new possibilities for educational technologies; leveraging AI in STEM education can revolutionize traditional learning approaches. In this paper we present a digital assistant prototype designed to support students engaged in the physics pendulum experiment. The prototype has been shown to effectively discern user intentions and generate pertinent replies, resulting in a smooth and achieving learning experience. The proposed AI assistant has the potential to be a useful platform for promoting interaction and communication with students in several educational contexts. The assistant's general functioning has been shown to improve by its integration with ChatCPT, which also raises its potential that it may be a useful tool for engagement and information sharing across a variety of educational use cases. This paper also discusses the potential challenges and ethical considerations associated with the use of AI in education, emphasizing the importance of responsible AI deployment to ensure equitable access and fairness for all learners. The findings and insights presented in this paper contribute to the ongoing research and development of AI-based educational technologies and highlight the role of AI agents in STEM education.
Digital and Socio Emotional Benefits of the Students and the Teachers from the Implementation of a Steam Education Project

The subject of this paper is related to the digital skills with which the Greek student must now be equipped, even at the level of Primary Education. The digital transformation of the Greek primary school is now necessary, a fact that was accelerated in recent years by the health crisis of the Covid-19 pandemic [1, 2]. The need to transform the school unit into a digital learning organization [3, 4] is imperative in order to equip students with digital skills of the 21st century, so as to be able to cope more effectively with the new educational conditions. This work presents the improvement of digital skills - alongside the socio-emotional - skills of the students and the teachers acquired during the implementation of steam education projects. The implemented steam education project created by the group of the robotics club of the Experimental Elementary School of the University of Patras, entitled “Ultrasonic glasses for obstacle detection”. Therefore, in the context of the implementation of steam education projects and utilizing the theories of design thinking methodology and problem solving in the specific project that will be presented, the digital skills of both the students and the teachers of the specific educational group were developed. At the same time, it emerged that in educational units, which operate as learning organizations, innovative practices, and activities find space to develop.
Sentiment Analysis on Karnatakaelection2023 Twitter Dataset Using Machine Learning

Platforms for social networking, such as Twitter have a ton of user-generated content that users can access. The global user base's ability to exchange thoughts, feelings, and perspectives on a variety of topics, increases the value of sentiment analysis in predicting outcomes and choosing the best course of action as needed. Users of the social networking and microblogging service namely twitter can send status updates with a maximum of 140 characters. Considering the sentiments expressed in the tweets in the context is one of the important current usage. Sentiment analysis is used to identify and categorize opinions as positive, neutral, or negative by separating them from different content formats including news, audits, and articles. The present work composed a dataset using the Twitter API based on the tweets of the Karnataka election in 2023. The dataset was then pre-processed to make it useful for the use of machine learning methods. Naive Bayes Classifier machine learning classification algorithm was used to classify the tweets into the desired attitudes. With the Naive Bayes Classifier, 82.05% accuracy was achieved. The outcome of the proposed work will be helpful in tracking social media to get insights into the upcoming activities.
A Graph-Based Approach for Representing Water Treatment Process Knowledge

The article describes a digital representation of knowledge related to industrial water treatment processes, with an aim of supporting knowledge querying. To this purpose, available industrial knowledge about physical and logical entities and their interrelations and interactions have been reviewed, processed, and organized as a Knowledge Graph. Production, operational and meta information, including targets, and quality/performance criteria have also been represented. Already available water quality data have been connected to nodes of this KG. As a result, the KG can enable the integration of heterogeneous, even unstructured – data from diverse sources (sensors, databases, enterprise resource planning systems (ERPs), etc.), therefore, allowing for holistic knowledge and data searches and querying.
Assessing the Human Ability to Recognize Synthetic Speech in Ordinary Conversation

This work assesses the human ability to recognize synthetic speech (deepfake). This paper describes an experiment in which we communicated with respondents using voice messages. We presented the respondents with a cover story about testing the user-friendliness of voice messages while secretly sending them a pre-prepared deep fake recording during the conversation. We examined their reactions, knowledge of deepfakes, or how many could correctly identify which message was deepfake. The results show that none of the respondents reacted in any way to the fraudulent deepfake message, and only one retrospectively admitted to noticing something specific. On the other hand, a voicemail message that contained a deepfake was correctly identified by 83.9% of respondents after revealing the nature of the experiment. Thus, the results show that although the deepfake recording was clearly identifiable among others, no one reacted to it. In summary, we show that the human ability to recognize voice deepfakes is not at a level we can trust. It is very difficult for people to distinguish between real and fake voices, especially if they do not expect them.
Fuzzy Signature with Biometric-Independent Verification

Just as biometric authentication has replaced conventional authentication methods to improve convenience and security, fuzzy signatures (FS), a technique that replaces the signing key of a digital signature with biometric information, has been studied. This paper proposes biometric-independent fuzzy signature (BIFS), a novel FS primitive that enables biometric-independent verification, which is inherently unsolvable with conventional FS. To circumvent that problem, BIFS generates signatures through a two-party protocol using biometric information and helper keys. We also propose a construction of BIFS that can easily replace existing signature services because the signature and verification keys are in the same form as those of digital signatures.
Mnemonic Phrase Management and SIM Based Two-Factor Authentication (2FA) for Mobile Wallets in Blockchain

Blockchain has grown for various IT businesses and an exchange of cryptocurrencies. The cryptocurrency wallets are essential for storing and managing the digital assets and cryptocurrencies, especially for mobile users. However, the security concerns of the mobile devices and wallets hinder the wide adoption of blockchain technology on mobile devices. Therefore, there is a need of a highly secured mobile wallet for blockchain technology. To address this issue, this paper proposes an architecture for key management in blockchain based mobile wallet. It involves mobile SIM card, as a hardware component and mnemonic phrase to achieve Two-Factor Authentication (2FA). It applies Shamir's secrete sharing scheme in order to enhance the wallet security and robustness. The security analysis of the proposed architecture is performed by considering different attacks.
A RISE-Based Explainability Method for Genuine and Impostor Face Verification

Heat Map (HM)-based explainable Face Verification (FV) has the goal to visually interpret the decision-making of black-box FV models. Despite the impressive results, state-of-the-art FV explainability methods based on HMs mainly address genuine verification by generating visual explanations that reveal the similar face regions which most contributed for acceptance decisions. However, the similar face regions may not be the unique critical regions for the model decision, notably when rejection decisions are performed. To address this issue, this paper proposes a more complete FV explainability method, providing meaningful HM-based explanations for both genuine and impostor verification and associated acceptance and rejection decisions. The proposed method adapts the RISE algorithm for FV to generate Similarity Heat Maps (S- HMs) and Dissimilarity Heat Maps (D-HMs) which offer reliable explanations to all types of FV decisions. Qualitative and quantitative experimental results show the effectiveness of the proposed FV explainability method beyond state-of-the-art benchmarks.
Enhancing Financial Fraud Detection in Bitcoin Networks Using Ensemble Deep Learning

This paper introduces an innovative approach to bolster financial fraud detection within the Bitcoin network using ensemble deep learning models. By synergistically merging ensemble techniques with state-of-the-art deep learning methodologies, this study creates a robust framework that enhances security and trust in financial systems. Through meticulous data preprocessing and feature engineering, the proposed ensemble model, comprising Multi-Layer Perceptron (MLP), Feedforward Neural Network (FNN), and Attention LSTM, undergoes comprehensive training and evaluation. Results underscore the ensemble's remarkable performance, surpassing individual models in accuracy, precision, and recall. Notably, the ensemble achieves an accuracy of 99.62%, along with exceptional precision and recall values exceeding 99%. These outcomes validate the ensemble's capacity to detect both fraudulent and legitimate transactions with unprecedented accuracy. By effectively combining advanced machine learning techniques with the intricacies of blockchain-based transactions, this research contributes to building a more secure and reliable financial ecosystem. The findings open avenues for future research, emphasizing the potential of ensemble deep learning models to fortify defenses against evolving financial fraud strategies, fostering trust and integrity in digital transactions.
Impact of Image Context for Single Deep Learning Face Morphing Attack Detection

The increase in security concerns due to technological advancements has led to the popularity of biometric approaches that utilize physiological or behavioral characteristics for enhanced recognition. Face recognition systems (FRSs) have become prevalent, but they are still vulnerable to image manipulation techniques such as face morphing attacks. This study investigates the impact of the alignment settings of input images on deep learning face morphing detection performance. We analyze the interconnections between the face contour and image context and suggest optimal alignment conditions for face morphing detection.
A Unified Framework for Querying Dynamic and Semantic Data Sources

Effective storing and querying of building energy consumption data is crucial, since the energy sector accounts for almost 40% of the energy consumed worldwide. However, the rapid growth of networking capabilities has led to the storage needs for energy consumption data in data lakes and warehouses becoming unmanageable, making querying an inefficient task. To effectively store such vast amounts with regard to their dimensions and their semantic interpretation, many semantic ontology models have been developed. Data sharing mechanisms are then applied on top of data warehouses to extract useful knowledge for user related purposes. It is recognized, though, that distributed query engines are capable of querying only dynamic big data, while semantic mechanisms are confined to querying semantic ontologies. In this paper, we propose a framework that facilitates efficient querying of heterogenous data sources of dynamic and semantic data including graph ontologies for metadata representation. The framework also establishes high level visualization services by employing well-known visualization tools. Finally, particular emphasis is placed on securing the framework with proper resource identity management. The framework is developed in the context of DigiBUILD, an Horizon Europe funded project aiming at providing a digital logbook for analytic services on the building sector.
Fairness and Privacy in Voice Biometrics: A Study of Gender Influences Using wav2vec 2.0

This study investigates the impact of gender information on utility, privacy, and fairness in voice biometric systems, guided by the General Data Protection Regulation (GDPR) mandates, which underscore the need for minimizing the processing and storage of private and sensitive data, and ensuring fairness in automated decision-making systems. We adopt an approach that involves the fine-tuning of the wav2vec 2.0 model for speaker verification tasks, evaluating potential gender-related privacy vulnerabilities in the process. Gender influences during the fine-tuning process were employed to enhance fairness and privacy in order to emphasise or obscure gender information within the speakers' embeddings. Results from VoxCeleb datasets indicate our adversarial model increases privacy against uninformed attacks, yet slightly diminishes speaker verification performance compared to the non-adversarial model. However, the model's efficacy reduces against informed attacks. Analysis of system performance was conducted to identify potential gender biases, thus highlighting the need for further research to understand and improve the delicate interplay between utility, privacy and equity in voice biometric systems.
Not-in-Perspective: Towards Shielding Google's Perspective API Against Adversarial Negation Attacks

The rise of cyberbullying in social media platforms involving toxic comments has escalated the need for effective ways to monitor and moderate online interactions. Existing solutions of automated toxicity detection systems, are based on a machine or deep learning algorithms. However, statistics-based solutions are generally prone to adversarial attacks that contain logic based modifications such as negation in phrases and sentences. In that regard, we present a set of formal reasoning-based methodologies that wrap around existing ma-chine learning toxicity detection systems. Acting as both pre-processing and post-processing steps, our formal reasoning wrapper helps alleviating the negation attack problems and significantly improves the accuracy and efficacy of toxicity scoring. We evaluate different variations of our wrapper on multiple machine learning models against a negation adver-sarial dataset. Experimental results highlight the improvement of hybrid (formal reasoning and machine-learning) methods against various purely statistical solutions.
Exploiting Face Recognizability with Early Exit Vision Transformers

Face recognition with Deep Learning is generally approached as a problem of capacity. The field has seen progressively deeper, more complex models or larger, more highly variant datasets. However, the carbon footprint of machine learning (ML) is a concern. A real push is developing to reduce the energy consumption of ML as we strive for a more eco-friendly society. Lower energy consumption or compute budget is always desirable, if accuracy is not reduced below a usable level. We present an approach using the state of the art Vision Transformer and Early Exits for reducing compute budget without significantly affecting performance. We develop a system for face recognition and identification with a closed-set gallery and show that with a small reduction in performance, a reasonable reduction in FLOPs can be obtained using our method.
Case Study on Cryptography

Data security is crucial in modern communication and technology to protect sensitive information from theft, loss, or unauthorized access. Cryptography is widely used to ensure data security by converting data into an unintelligible format that can only be decoded using predefined algorithms known only to the intended recipients. Cryptography offers several benefits for data security, including confidentiality, authentication, integrity, availability, and identification of user data. Encryption and digital signatures are two common forms of cryptography. Encryption scrambles data into a format that can only be decoded by authorized individuals, while digital signatures authenticate the identity of the sender and ensure the data's integrity. The increasing use of the internet for communication and business has led to a rise in the risk of data theft and loss. Cybercriminals use various techniques to steal sensitive information, making it necessary to use cryptography to protect data from unauthorized users and cybercriminals. Cryptography provides essential tools for protecting sensitive information in the modern age. It ensures that data remains secure and can only be accessed by authorized individuals while offering benefits such as confidentiality, authentication, integrity, availability, and identification of user data.