Reviewerly/exHarmony huggingface dataset:
State Super Sampling Soft Actor–Critic Algorithm for Multi-AUV Hunting in 3D Underwater Environment

Reinforcement learning (RL) is known for its efficiency and practicality in single-agent planning, but it faces numerous challenges when applied to multi-agent scenarios. In this paper, a Super Sampling Info-GAN (SSIG) algorithm based on Generative Adversarial Networks (GANs) is proposed to address the problem of state instability in Multi-Agent Reinforcement Learning (MARL). The SSIG model allows a pair of GAN networks to analyze the previous state of dynamic system and predict the future state of consecutive state pairs. A multi-agent system (MAS) can deduce the complete state of all collaborating agents through SSIG. The proposed model has the potential to be employed in multi-autonomous underwater vehicle (multi-AUV) planning scenarios by combining it with the Soft Actor–Critic (SAC) algorithm. Hence, this paper presents State Super Sampling Soft Actor–Critic (S4AC), which is a new algorithm that combines the advantages of SSIG and SAC and can be applied to Multi-AUV hunting tasks. The simulation results demonstrate that the proposed algorithm has strong learning ability and adaptability and has a considerable success rate in hunting the evading target in multiple testing scenarios.
An approach to automatic classification of hate speech in sports domain on social media

Abstract Hate Speech encompasses different forms of trolling, bullying, harassment, and threats directed against specific individuals or groups. This phenomena is mainly expressed on Social Networks. For sports players, Social Media is a means of communication with the widest part of their fans and a way to face different cyber-aggression forms. These virtual attacks can harm players, distress them, cause them to feel bad for a long time, or even escalate into physical violence. To date, athletes were not observed as a vulnerable group, so they were not a subject of automatic Hate Speech detection and recognition from content published on Social Media. This paper explores whether a model trained on the dataset from one Social Media and not related to any specific domain can be efficient for the Hate Speech binary classification of test sets regarding the sports domain. The experiments deal with Hate Speech detection in Serbian. BiLSTM deep neural network was learned with different parameters, and the results showed high Precision of detecting Hate Speech in sports domain (96% and 97%) and pretty low Recall.
Data security of machine learning applied in low-carbon smart grid: A formal model for the physics-constrained robustness

Towards the low-carbon goal, a smart grid features remote connection, data sharing, and cyber–physical integration to increase the flexibility of energy supplies, to reduce electricity wastage, and to enhance safety under intermittent renewables. In this context, machine learning (ML) is increasingly employed in smart grids to solve tasks that require deep data analytics. However, it is well recognized that ML models are vulnerable to adversarial examples that might be contained in open shared data, which creates opportunities for potential attackers to launch cyberattacks. In particular, system operating states acting as critical inputs to ML-based smart grid applications (MLsgAPPs) can be manipulated by malicious actors to mislead the system operator into making wrong decisions that in turn may cause major blackouts and other damaging cascading events. It is thus imperative to advance vulnerability assessment for MLsgAPPs. In this paper, we propose a physics-constrained robustness evaluation framework for MLsgAPPs, which is a first attempt of using tree ensemble (TE) models. Unlike adversarial attacks against prior studied image-classification tasks, adversarial examples against TE-based smart grid applications (TEsgAPPs) must not only cheat human intuition but also follow laws of physics and bypass error-checking mechanisms for power systems. Thus, formulation of the traditional robustness evaluation problem must be reconsidered to account for domain-specific misclassification, physical limit, power balance, and error-bypass constraints, in order to obtain tight lower bounds of the magnitudes of tolerated adversarial perturbations. We adopt a formal modeling approach to construct a discrete tree structure and to specify the model evasion conditions. We propose an efficient robustness evaluation method by transforming variables considering ℓ1 and ℓ∞ norms, followed by a generalization that expands the variable space to admit other types of norms. Using TE-based power system security assessment as an example, comprehensive simulations are conducted for evaluating the physics-constrained robustness under different TE models and model parameters.
Classification of incunable glyphs and out-of-distribution detection with joint energy-based models

Abstract Optical character recognition (OCR) has proved a powerful tool for the digital analysis of printed historical documents. However, its ability to localize and identify individual glyphs is challenged by the tremendous variety in historical type design, the physicality of the printing process, and the state of conservation. We propose to mitigate these problems by a downstream fine-tuning step that corrects for pathological and undesirable extraction results. We implement this idea by using a joint energy-based model which classifies individual glyphs and simultaneously prunes potential out-of-distribution (OOD) samples like rubrications, initials, or ligatures. During model training, we introduce specific margins in the energy spectrum that aid this separation and explore the glyph distribution’s typical set to stabilize the optimization procedure. We observe strong classification at 0.972 AUPRC across 42 lower- and uppercase glyph types on a challenging digital reproduction of Johannes Balbus’ Catholicon , matching the performance of purely discriminative methods. At the same time, we achieve OOD detection rates of 0.989 AUPRC and 0.946 AUPRC for OOD ‘clutter’ and ‘ligatures’ which substantially improves upon recently proposed OOD detection techniques. The proposed approach can be easily integrated into the postprocessing phase of current OCR to aid reproduction and shape analysis research.
TIMIT-TTS: A Text-to-Speech Dataset for Multimodal Synthetic Media Detection

With the rapid development of deep learning techniques, the generation and counterfeiting of multimedia material has become increasingly simple. Current technology enables the creation of videos where both the visual and audio contents are falsified. While the multimedia forensics community has begun to address this threat by developing fake media detectors. However, the vast majority existing forensic techniques only analyze one modality at a time. This is an important limitation when authenticating manipulated videos, because sophisticated forgeries may be difficult to detect without exploiting cross-modal inconsistencies (e.g., across the audio and visual tracks). One important reason for the lack of multimodal detectors is a similar lack of research datasets containing multimodal forgeries. Existing datasets typically contain only one falsified modality, such as deepfaked videos with authentic audio tracks, or synthetic audio with no associated video. Currently, datasets are needed that can be used to develop, train, and test these forensic algorithms. In this paper, we propose a new audio-visual deepfake dataset containing multimodal video forgeries. We present a general pipeline for synthesizing deepfake speech content from a given video, facilitating the creation of counterfeit multimodal material. The proposed method uses Text-to-Speech (TTS) and Dynamic Time Warping (DTW) techniques to achieve realistic speech tracks. We use this pipeline to generate and release TIMIT-TTS, a synthetic speech dataset containing the most cutting-edge methods in the TTS field. This can be used as a standalone audio dataset, or combined with DeepfakeTIMIT and VidTIMIT video datasets to perform multimodal research. Finally, we present numerous experiments to benchmark the proposed dataset in both monomodal (i.e., audio) and multimodal (i.e., audio and video) conditions. This highlights the need for multimodal forensic detectors and more multimodal deepfake data.
Public key exchange protocols based on tropical lower circulant and anti circulant matrices

&lt;abstract&gt;&lt;p&gt;In recent years, many efficient key exchange protocols have been proposed based on matrices over the tropical semirings. The tropical addition of two elements is the minimum of the elements, while the tropical multiplication is the sum of the two elements. This paper proposes a novel key exchange protocol based on the min-plus semiring ($ \mathbb{Z}\cup\{\infty\}, \oplus, \otimes $) by introducing anti-$ s $-$ p $-circulant matrices, which forms a commutative subset of $ M_{n \times n}(\mathbb{Z}\cup \{\infty\}) $. We have given further analysis of the protocol in detail using upper or lower-$ s $-circulant matrices. Additionally, we prove that the set of all lower-$ s $-circulant matrices is a sub-semiring of the tropical semiring $ M_{n \times n}(\mathbb{Z}\cup \{\infty\}) $. We discuss the detailed security analysis of the protocol with upper or lower-$ s $-circulant matrices and provide cryptographic algorithms for both key exchange protocols with detailed explanations. We compare the protocol based on upper or lower-$ s $-circulant matrices and our proposed protocol in terms of time complexity and memory usage. Finally, we analyse the security and show that our protocol is safe against popular attacks of tropical key exchange protocols. The security of these protocols relies on the difficulty of solving tropical non-linear equations.&lt;/p&gt;&lt;/abstract&gt;
Blockchain-Based Business Process Management (BPM) for Finance: The Case of Credit and Claim Requests

Because of the competitive economy, organizations today seek to rationalize, innovate, and adapt to changing environments and circumstances as part of business process improvement efforts. The strength of blockchain technology lies in its usage as an apt technology to enhance the efficiency and effectiveness of business processes; furthermore, it prevents the use of erroneous or obsolete data and allows sharing of confidential data securely. The use of superior technology in the execution and automation of business processes brings opportunities to rethink the specific process itself as well. Business processes modeling and verification are essential to control and assure organizational evolution, therefore, the aim of this paper is three-fold: firstly, to provide business process management patterns in finance, based on blockchain, specifically for the loan-application process in the banking industry and claim process in the insurance industry that could be used and customized by companies; secondly, to critically analyze challenges and opportunities from the introduction of such approach for companies, and thirdly, to outline how companies can implement the loan business process as a web service. Partner companies (a bank and an insurance company) formulated the potential requirements for M2P along with the application of blockchain technology. An experimental design framework was established that gave the necessary services to model the requirements, check the models, and operationalize the models. The applied research methodologies are as follows: design science research paradigm and software case study, model-to-programming (M2P) of business processes, and utilization of patterns of workflow and blockchain.
iAOI: An Eye Movement Based Deep Learning Model to Identify Areas of Interest

Eye Tracking is an important research technique used to analyze the movement of the eye and to recognize a pattern. Eye Tracking is a frequently used tool to understand the prognosis of a disease. iAOI is an Artificial Neural Network model that predicts the Area or Region of Interest viewed by a participant depending on the eye movement data. An eye-tracking experiment is conducted for participants with Parkinson’s Disease and healthy controls for visual search tasks. From the eye movements recorded a higher-order dataset based on the Area of Interest is derived. This dataset is explored to understand the underlying AOI patterns for participants with Parkinson’s Disease. This prediction from iAOI help in understanding the ability to search for a region of interest by patients suffering from Parkinson’s Disease. iAOI predicts the viewed region of interest and how it deviates from the intended Area of Interest. iAOI provides offbeat visualizations that depict the higher-order Area of Interest. By applying the ANN model for this multi-class classification, an accuracy of 83% was observed.
A Blockchain-Driven Framework for Issuance of NFT-Based Warranty to Customers on E-Commerce

With the advent of digitalization, online shopping has become increasingly popular as it saves buyers and retailers time by providing information about different products and brands at their doorstep. Even the platforms offering second-hand products have seen massively trafficked over the past few years. However, online shopping experiences are not always smooth customers have to deal with security concerns which makes it difficult to trust online retailers. Customers going for second-hand products find it hard to get product-related accurate details and access warranty services after purchase. In-addition, existing e-commerce platforms does not offer jointly e-commerce cum re-commerce. Hence, a transparent E-commerce platform system is required to enable users to conveniently and securely buy/sell the product. Thus, we proposed a Blockchain-based e-commerce solution that provides users with an NFT (digital warranty card) just after the transaction. This digitalization of documentation will ease the process of accessing customers' product details for repair/replacement services. Moreover, the proposed system supports re-commerce along with e-commerce providing the user the feature to add their product in the flea market where other users can choose and view the purchase history of the product. Based on that, they can negotiate with the seller and transfer the product ownership to their name. We implemented the proposed scheme using reactJS, Django, MySQL, and blockchain. Smart contracts are deployed over the polygon network, interacting with web pages developed using React. Finally, the analysis and results of the proposed model express that it is viable, secure, and far better than the existing e-commerce platforms.
iSTIMULI: Prescriptive Stimulus Design for Eye Movement Analysis of Patients with Parkinson’s Disease

With an increase in eye-tracking applications, there is a need for eye gaze data analytics to widen its scope to provide customized solutions. However, among several types of analytics such as predictive, descriptive, diagnostic, and prescriptive analytics providing insight into it is quite challenging. This work presents the need and role of prescriptive data analysis that can be carried out on eye gaze data pertaining to a specific study of research on the diagnosis of Parkinson’s disease patients during visual search and visual attention tasks. We further look at the various aspects like the image stimulus used, a task performed by the viewer, and its relation to the viewer’s eye movement behavior. iSTIMULI includes the design of the image stimuli used to classify the eye movements of Parkinson’s disease patients from healthy controls. Visualizations of eye movements on the image stimulus designed are presented. A machine learning analysis is also provided that substantiates the significance of the stimuli with the highest f-measure of 73%. iSTIMULI establishes a relation between image stimulus and viewer behavior. As a prescriptive analysis model, iSTIMULI calculates the risk based on eye movements and recommends actions to be taken in the future to improve the ability of the participant.
Toward Semantic Communication Protocols: A Probabilistic Logic Perspective

Classical medium access control (MAC) protocols are interpretable, yet their task-agnostic control signaling messages (CMs) are ill-suited for emerging mission-critical applications. By contrast, neural network (NN) based protocol models (NPMs) learn to generate task-specific CMs, but their rationale and impact lack interpretability. To fill this void, in this article we propose, for the first time, a semantic protocol model (SPM) constructed by transforming an NPM into an interpretable symbolic graph written in the probabilistic logic programming language (ProbLog). This transformation is viable by extracting and merging common CMs and their connections, while treating the NPM as a CM generator. By extensive simulations, we corroborate that the SPM tightly approximates its original NPM while occupying only 0.02% memory. By leveraging its interpretability and memory-efficiency, we demonstrate several SPM-enabled applications such as SPM reconfiguration for collision-avoidance, as well as comparing different SPMs via semantic entropy calculation and storing multiple SPMs to cope with non-stationary environments.
EID: Facilitating Explainable AI Design Discussions in Team-Based Settings

Artificial intelligence (AI) systems have many applications with tremendous current and future value to human society. As AI systems penetrate the aspects of everyday life, a pressing need arises to explain their decision-making processes to build trust and familiarity among end users. In high-stakes fields such as healthcare and self-driving cars, AI systems are required to have a minimum standard for accuracy and to provide well-designed explanations for their output, especially when they impact human life. Although many techniques have been developed to make algorithms explainable in human terms, no design methodologies that will allow software teams to systematically draw out and address explainability-related issues during AI design and conception have been established. In response to this gap, we proposed the explainability in design (EID) methodological framework for addressing explainability problems in AI systems. We explored the literature on AI explainability to narrow down the field into six major explainability principles that will aid designers in brainstorming around the metrics and guide the critical thinking process. EID is a step-by-step guide to AI design that has been refined over a series of user studies and interviews with experts in AI explainability. It is devised for software design teams to uncover and resolve potential issues in their AI products and to simply refine and explore the explainability of their products and systems. The EID methodology is a novel framework that aids in the design and conception stages of the AI pipeline and can be integrated into the form of a step-by-step card game. Empirical studies involving AI system designers have shown that EID can decrease the barrier of entry and the time and experience required to effectively make well-informed decisions for integrating explainability into their AI solutions.
Stacked cross-modal feature consolidation attention networks for image captioning

The attention-enriched encoder-decoder framework has recently aroused great interest in image captioning due to its overwhelming progress. Many visual attention models directly leverage meaningful regions to generate image descriptions. However, seeking a direct transition from visual space to text is not enough to generate fine-grained captions. This paper exploits a feature-compounding approach to bring together high-level semantic concepts and visual information regarding the contextual environment fully end-to-end. Thus, we propose a stacked cross-modal feature consolidation (SCFC) attention network for image captioning in which we simultaneously consolidate cross-modal features through a novel compounding function in a multi-step reasoning fashion. Besides, we jointly employ spatial information and context-aware attributes (CAA) as the principal components in our proposed compounding function, where our CAA provides a concise context-sensitive semantic representation. To better use consolidated features potential, we propose an SCFC-LSTM as the caption generator, which can leverage discriminative semantic information through the caption generation process. The experimental results indicate that our proposed SCFC can outperform various state-of-the-art image captioning benchmarks in terms of popular metrics on the MSCOCO and Flickr30K datasets.
Measuring Presence and Situational Awareness in a Virtual Reality Flight Simulator

Abstract: A sense of presence plays a significant role in virtual reality (VR) usability, yet there is limited research of its existence in a virtual reality flight simulator (VRFS). Similarly, there is scarce investigation of situational awareness (SA) in a VRFS. Consequently, this paper discusses an experiment that explored presence and SA in a VRFS. Physiological responses are also captured. The results reveal that participants felt a high degree of presence in a VRFS, and felt immersed and involved. The sense of presence contributed to good SA, and physiological data identified increased stress levels. These results are an important step in the exploration of VR in the field of aviation training. However, more research is required to identify how and where VR could be applied.
A CAD System Based On a Stacked Ensemble Model and ML Techniques for Breast Cancer Prognosis

breast cancer is the most spread cancer globally. The disease symptoms and treatments differ from one patient to another. Early detection can considerably alter the outcome for breast cancer patients. CAD systems playa fundamental role in the prognosis of the disease. In this paper, a proposed ensemble model based on the stacking technique uses logistic regression LR as a Meta-model and four machine learning algorithms Decision Tree DT, Random Forest RF, extreme gradient boosting XGboost, and adaptive boosting Adaboost as base models. The data used in this study is the breast cancer Wisconsin dataset. After preprocessing and features selection, the models were trained and then tested. Evaluation metrics were calculated to evaluate the system's global performance. Results indicate that the stacking techniques can enhance the performance of the system. The ensemble model outperformed the base models with an accuracy of over 98% and a precision of 100% which makes it of service for radiologists in breast tumor classification tasks.
About Time: On the Challenges of Temporal Guarantees in Untrusted Environments

Measuring the passage of time and taking actions based on such measurements is a common security-critical operation that developers often take for granted. When working with confidential computing, however, temporal guarantees become more challenging due to trusted execution environments residing in effectively untrusted environments, which can oftentimes influence expectations on time and progress. In this work, we identify and categorize five different levels of tracking the passage of time that an enclave may be able to mesure or receive from its environment. Focusing first on the popular Intel SGX architecture, we analyze what level of time is possible and how this is utilized in both academia and industry projects. We then broaden the scope to other popular trusted computing solutions and list common applications for each level of time, concluding that not every use case requires an accurate access to real-world time.