Reviewerly/exHarmony huggingface dataset:
Quantum annealing solution for the unrelated parallel machine scheduling with priorities and delay of task switching on machines

Quantum computing has emerged in recent years as an alternative to classical computing, which could improve the latter in solving some types of problems. One of the quantum programming models, Adiabatic Quantum Computing, has been successfully used to solve problems such as graph partitioning, traffic routing, and task scheduling. In this paper, the focus is on the scheduling of the problem of unrelated parallel machines, where the processing time of tasks on any of the available processing elements is known. Moreover, the proposed model is extended in two relevant aspects for this kind of problem: the existence of some degree of priority of tasks, and the introduction of a delay or penalty every time a processing unit or machine changes the type of task that executes. In all cases, the problem is expressed as Quadratic Unconstrained Binary Optimization, which can be subsequently solved using quantum annealers. The quantum nonlinear programming framework discussed in this work consists of three steps: quadratic approximation of cost function, a binary representation of parameter space, and solving the resulting Quadratic Unconstrained Binary Optimization on the quantum annealer platform D-Wave. One of the novelties in tackling this problem is the compaction of the model bearing in mind the repetitions of each task, to allow solving larger scheduling problems with the quantum resources available in the experimentation platform. An estimation of the number of qubits required in relation to the scheduling parameters is analyzed. The models have been implemented on the D-Wave platform and validated with respect to other traditional methods. Furthermore, the proposed extensions to consider priorities and to switch the delay of tasks have been analyzed using a case study.
Games for Development of Preschool Children's Creativity

The purpose of the present study is to trace the impact of games developed according to the “theory of inventive problem solving” on the creativity of preschool children. The characteristics of creativity and its main components are described in the article. Based on these components were developed criteria and indicators derived for evaluating the degree of creativity displayed by children. The main features of the “theory of inventive problem solving” are also reviewed and exemplary game designs that incorporate elements of the theory are proposed. It was tracked to what extent the children participants in the research show creativityand creative thinking in the process of developing the games based on the “theory of inventive problem solving”. The obtained results show that this type of games favor the development of creativity of 5 – 7-year-old children.
Specificity of the Competences of Specialists in the Field of Social-Pedagogical Work

The present work is devoted to the task of making an overview and analysis of the problem of formation of competences among specialists in sociopedagogical practice. The state of development on this topic among researchers is reviewed. The essential and substantive characteristics of the competence are summarized. Its specific aspects are distinguished – knowledge, skills and notions/attitudes. Focus is placed on the essence of the competencies that are necessary in the preparation of specialists in the field of socio-pedagogical work. A process for improving and developing competence as a social and professional phenomenon is proposed.
Modeling an Educational Environment Promoting the Development of Key Competencies in Preschool Age

A supportive, motivating and purposefully organized educational environment can become a powerful factor for laying the foundations for the development of important qualities, skills and competences in children of preschool age. The article aims to: reveal the possibilities of the educational environment and promote the development of key competences for lifelong learning in preschool children. Tasks: 1. To study and analyze literary sources and normative documents related to the application of the competence approach in preschool education; 2. To indicate the main principles underlying the creation of an educational environment promoting the development of key competencies in kindergarten; 3. To highlight the possibilities for modeling an educational environment to promote the development of key competences in preschool age
Video Surveillance Authentication: Real-Time ENF Signal Hiding at the Edge

Due to the significance of the visual information exchanged in Internet of Video Things (IoVT) networks, attackers are constantly launching new attacks and attempt to exploit new vulnerabilities. One of the most common and difficult-to-prevent attacks on the Visual Layer is the Frame Duplication Attack (FDA). Recently, two techniques were proposed for FDA detection at the edge by using the embedded Electrical Network Frequency (ENF) signals in an effort to surpass limitations of conventional passive methods. In this paper, a Real-Time ENF signal hiding technique at the edge is proposed. Our motivation is to examine the possibility of authenticating the surveillance feed by hiding the ENF signal. Experiments are conducted, including an extensive performance comparison between the proposed and reference encoder, a feasibility study for the proposed encoder’s integration to a Raspberry Pi for video streaming purposes and finally the implementation of a proof-of-concept prototype. According to the findings, the proposed approach provides real-time FDA detection at reduced computational complexity and hardware requirements, thus rendering this method appropriate for applications at the edge.
Federated Deep Feature Extraction-based SLAM for Autonomous Vehicles

In this paper we investigate the impact of federated learning on deep learning-based feature extraction used for self-localization in autonomous vehicles. The accurate and reliable determination of the vehicle’s position is crucial for both safety and efficiency purposes. Deep-learning based feature extractors have demonstrated benefits over model-based components and have started to replace them during the past few years. In addition, Federated Learning (FL) frameworks can provide advantages in terms of data privacy, resource allocations, and scalability. We apply FL principles on training a deep learning-based feature detector network and we evaluate the performance of the models by integrating them into a Simultaneous Localization and Mapping (SLAM) system and measuring the drift of generated estimated trajectories. The results showed us that the FL-based methods can perform similarly and sometimes even better in comparison to traditional centralized solutions.
SGraphZoe: Explainable self-supervised framework for signal-based anomaly detection

Signal-based anomaly detection is a recurring problem that has drawn the attention of many research projects and resulted in the development of multiple solutions. One of the main obstacles to anomaly detection is the rarity of the occurrences of interest. Extremely small amount of labelled data is troublesome from the training perspective since it has a detrimental influence on the accuracy of predictions. The second challenge is providing a clear and understandable model. Answering this second issue is particularly important for a variety of industries since it is beneficial to understand what causes outliers in order to avoid them in the future. To address the aforementioned concerns, we propose a novel self-supervised framework named SGraphZoe which outperforms linear semi-supervised state-of-the-art outlier detection algorithms while maintaining transparency throughout training and prediction steps. This framework is built on a Self-supervised strategy and combines a semi-supervised (Graph Diffusion & PCA) and a supervised (Zoetrope Genetic Programming) algorithms.
Could Human Gaze Augment Detectors of Synthetic Images?

Recent advances in generative adversarial networks (GANs) allow for the synthesis of extremely photo-realistic face images, deceiving even the most experienced observers, let alone the unsuspecting internet user. Due to this, there has been a considerable effort by the image forensics community to design appropriate tools for the detection of these images. This paper first implements one such detection technique based on spatial and cross-band co-occurrence matrices and convolutional neural networks (CNNs), and then attempts to improve it by introducing additional information obtained from the human gaze. We show that in cases where human observers correctly decide whether an image is real or fake, eye movement information in combination with spatial and cross-band co-occurrence matrices derived from observation regions can be informative towards the task of detecting fake images. However, only a limited increase in the detection accuracy is achieved.
Impact of Video Processing Operations in Deepfake Detection

The detection of digital face manipulation in video has attracted extensive attention due to the increased risk to public trust. To counteract the malicious usage of such techniques, deep learning-based deepfake detection methods have been developed and have shown impressive results. However, the performance of these detectors is often evaluated using benchmarks that hardly reflect real-world situations. For example, the impact of various video processing operations on detection accuracy has not been systematically assessed. To address this gap, this paper first analyzes numerous real-world influencing factors and typical video processing operations. Then, a more systematic assessment methodology is proposed, which allows for a quantitative evaluation of a detector’s robustness under the influence of different processing operations. Moreover, substantial experiments have been carried out on three popular deepfake detectors, which give detailed analyses on the impact of each operation and bring insights to foster future research.
Applications and Limits of Image-to-Image Translation Models

Image-to-image (I2I) translation models are widely employed in several fields, e.g., computer vision, security or medicine. Their goal is to map images from a source domain to a target domain while preserving content information. Despite their success, these models suffer from multiple weaknesses. For example, many practical scenarios do not consent to collect a sufficient amount of images, leading to imbalanced domains. Furthermore, mode collapse and training instability require a careful design and further discourage their deployment on edge devices. Finally, I2I models need an intensive computation to learn conditional probability distributions and are difficult to adapt to different contexts. These drawbacks mainly limit their large scale applicability. In this work, we want to shed light on the main solutions adopted to overcome the above issues and their impact on the performance. We also investigate several approaches to deploy these models on low-powered devices and weight sharing techniques to reduce the number of parameters and resources used.
Learned Image Compression with Wavelet Preprocessing for Low Bit Rates

Deep Learning has revolutionized the field of image processing and image compression in particular. A lot of research has been done in recent years on the subject of learning-based image compression, which has resulted in methods with increased compression performance but high computational complexity. The most successful methods eradicate the redundancies by using entropy modelling. In this paper, we utilize the Discrete Wavelet Transform (DWT) as a preprocessing step for a simple hyperprior model. The proposed method is compared to both traditional and deep learning-based techniques. It proves to be superior in lower bit rates, using both Peak Signal-to-Noise Ratio (PSNR) and Multi-Scale Structural Similarity (MS-SSIM) as metrics for the evaluation of the model.
TRAIT: A Trusted Media Distribution Framework

Trusted distribution and consumption of media content has become a challenging issue, especially with the advancement of machine learning-based techniques such as deep fake. To address such challenges, this paper proposes a new metadata schema which is embedded within a larger framework that facilitates trusted media distribution. This schema is realised through a distributed media blockchain core in conjunction with algorithms to detect media modifications. Such a framework is expected to improve trust in media consumption, ensuring media integrity, authenticity and provenance.
Building a Turkish Text-to-Speech Engine: Addressing Linguistic and Technical Challenges

The pursuit of end-to-end text-to-speech (TTS) systems has resulted in generating natural sounding speech. However, due to the requirements of high quality data and pronouncing dictionary, certain languages may face obstacles in achieving comparable results. In this paper, we present innovative solutions to these challenges in the context of Turkish TTS applications. To demonstrate the effectiveness of our approach, we train Fastspeech2 and Tacotron models and evaluated them using mean opinion score (MOS) measure and Speaker Encoder Cosine Similarity (SECS). Evaluation results show that FastSpeech2 model performed well than Tacotron, also our approaches for the dataset is improved the performance of FastSpeech2.
An Automated Ground-to-Aerial Viewpoint Localization for Content Verification

In many forensic examinations and media authenticity verifications, it is essential to reconstruct the place where a photo was taken without having information such as GPS coordinates and other metadata available. In recent years, satellite imagery has been used in some of these reconstructions to map the image captured from the ground with the photos from above. This task, known as ground-to-aerial mapping, allows a very effective localization but remains a complex and time-consuming task. Deep learning-based methods allow for accurate automatic image matching; however, many of these solutions can be challenging to explain and, therefore, barely applicable in scenarios where it is necessary to justify the analysis. Consequently, in this paper, we propose a fully automated, explicable solution that is able to perform image-matching tasks based on a graph-based solution. Our proposed pipeline is composed of four stages in which we extract a graph representation of the images that we use for matching. Moreover, the designed pipeline improves previous related methods of 17.84% for the mean IOU top-1 and of 32.71% for the top-3.
Towards an international standard to establish trust in media production, distribution and consumption

Advances in media content manipulation and artificially generated content pose new challenges to the assessment of media authenticity. While automated detection methods can provide meaningful insights and decision support in some scenarios, they cannot provide trustworthy and comprehensive information about the origin and provenance of media assets. Therefore, a longer-term approach should rather focus on secure and interoperable annotations related to the creation and provenance of media. In October 2020, the JPEG Committee initiated a standardization exploration named "JPEG Fake Media" to address these needs. Subsequently, since many of the requirements, for example related to secure annotation and identification of media assets, are also relevant to achieve interoperability in Non-Fungible Tokens (NFTs) an additional exploration was initiated, specifically focused on standardization needs for NFTs. In April 2022 a first Call for Proposals on JPEG Fake Media was issued. Based on the responses to the call, a new standardization project named JPEG Trust was initiated to specify an interoperable framework for establishing trust in media production, distribution, and consumption. This paper presents the journey of JPEG to leverage formal methods of standardization in this context, starting from the initial JPEG Fake Media exploration, followed by the subsequent consideration of NFT use cases and requirements, through to the commencement of the new JPEG Trust international standard.
Instructional Activity Detection Using Deep Neural Networks

Analyzing instructional videos via computer vision and machine learning holds promise for several tasks, such as assessing teacher performance and classroom climate, evaluating student engagement, and identifying biases in instruction. The traditional way of evaluating instructional videos depends on manual observation with human raters, which is time-consuming and requires a trained labor force. This paper tests several deep network architectures in the automation of instructional video analysis, where the networks are tailored to recognize classroom activity. Our experimental setup includes a set of 250 hours of primary and middle school videos that are annotated by expert human raters. We present several strategies to handle varying lengths of instructional activities, a major challenge in the detection of instructional activity. Based on the proposed strategies, we enhance and compare different deep networks for detecting instructional activity.
Evaluation of Virtual Reality via 360° videos Reusable e-Resources Embedded in Healthcare Curricula

Recent advancements in Virtual Reality (VR) immersive technologies provide new tools for the development of novel and promising applications for Medical Education courses. This paper presents an evaluation process for teaching the clinical skills course by exploiting the potential of a Virtual Reality application utilizing 360° videos with medical content for an undergraduate level course. We discuss the evaluation process, results and identify the potential role of Virtual Reality reusable e-resources into learning and practicing clinical skills and the concept of embedding these processes to a real assess-educate assess cycle that uses digital tools to optimize outcome. The development of VR technologies in recent years has resulted in more accessible and affordable solutions that can still provide promising results. The results of our analysis validated the effective design of a technologically enhanced learning tool as an addition to the existing methods used in medical education. Ultimately, the CoViRR analysis on the feasibility and acceptability of reusable virtual reality e-resources will serve as an example to other higher education institutions and faculty on techniques and topics for effective resource creation. Concluding, VR and interactive devices resulted in the development of holistic, portable, accessible, and usable systems for medical education.
Resource Efficient Federated Learning for Deep Anomaly Detection in Industrial IoT applications

Anomaly data constitute a thorny problem in numerous industrial applications. In recent years, deep learning enabled anomaly detection has emerged as a critical direction, however the improved detection accuracy and reconstruction is achieved with the utilization of large neural networks with more layers and nodes, increasing their storage and computational cost. Moreover, the data collected in edge devices contain user privacy, introducing challenges that can be successfully addressed by the very recent on-device privacy-preserving distributed machine learning paradigm, known as federated learning (FL). This paradigm allows edge devices to locally train and exchange models increasing also the communication cost. Thus, in order to deal with the increased communication, processing and storage challenges introduced by FL based deep anomaly detection NN pruning is expected to have significant benefits towards both reducing the processing, storage and communication complexity but also towards avoiding the over fitting problem. With this focus, a novel compression-based optimization problem is proposed at the server-side of a FL paradigm that simultaneously fusses the local models broadcast by the edge devices and performs pruning generating a much more compressed model to be deployed at the edge devices with no accuracy loss even if the training is performed using a small subset of data instances, achieving compression rates greater than 99%.
A lightweight ConvGRU network for Distracted Driving detection

In this paper, we explore the problem of automatic detection of dangerous or distracted driving using multi-modal cameras. A deep convolutional model with Gated Recurrent Unit (GRU) layers for classification on the Driver Anomaly Detection (DAD) dataset is proposed. The key features are the limited use of 3D convolutions and the replacement of 2D convolutions with depth-wise separable convolutions, which reduce the computational complexity of the model to a small fraction of previous architectures with a small decrease in AUC performance. In addition, the threshold for binary classification between safe and distracted driving is adaptively estimated through the training data. Finally, an ensemble of all multi-modal inputs yields the final classification with favourable performance.
A Data-driven Deep Learning Approach for Bitcoin Price Forecasting

Bitcoin as a cryptocurrency has been one of the most important digital coins and the first decentralized digital currency. We propose a shallow Bidirectional-LSTM (Bi-LSTM) model, fed with feature engineered data using our proposed method to forecast bitcoin closing prices in a daily time frame. We compare the performance with that of other forecasting methods, and show that with the help of the proposed feature engineering method, a shallow deep neural network out-performs other popular price forecasting models.
Masked Face Recognition Using Convolutional Neural Networks and Similarity Analysis

Nowadays, human face recognition systems have been widely used in different applications in which identity recognition is needed. The performance of current face recognition algorithms is negatively affected by occlusions, such as facial masks and various human poses. To address these challenges, we re-trained a modified version of the VGG19 deep learning model on masked and unmasked images of 62 identities to design a feature extractor that extracts deep features from the non-occluded areas of the face. This feature extractor is combined with our proposed similarity analysis network that is trained on our dataset to automatically judge whether the masked and unmasked images correspond to the same or different identities. Our final approach consists of a feature extractor from a fine-tuned VGG19 and a similarity model. It achieved an accuracy of 80 to 85 percent in recognizing the identity of test masked images with different poses.
Comparative Cryptocurrencies and Stablecoins Regulation: A Framework for a Functional Comparative Analysis

The financial applications of the blockchain technology are gaining increasing attention in the regulatory sphere both for their growing relevance and for the several scandals and failures of the past months. The regulatory landscape is quickly and non-linearly evolving, resulting in the impossibility to capture a nitid snapshot of the international regulatory regime from a comparative perspective. Therefore, this chapter takes a functional approach, investigating the sources and dynamics of regulatory convergence and divergence in the area of cryptocurrencies and stablecoins. The chapter focuses mostly, but not exclusively, on the regulatory regime of the EU, UK and US. This chapter adds to the literature on the regulation of crypto finance as it provides a functional framework to approach an area whose regulation is quickly evolving. Moreover, it also adds to the comparative law literature, looking at the sources and dynamics of divergence in the regulation of innovative technologies.