Reviewerly/exHarmony huggingface dataset:
Fingerprinting Smartphone Accelerometers with Traditional Classifiers and Deep Learning Networks

Fingerprinting smartphones using their accelerometers has several applications, including activity recognition, driving style classification and device to device authentication. In this work, we study accelerometer-based smartphone fingerprinting. We gather data from mobile devices placed together to record identical vibrations. Then, we extract time domain features, which we use to train multiple traditional machine learning algorithms based on statistical properties of the data. Finally, we use the raw data in a more complex Convolutional Neural Network and compare the results. To make the investigations more challenging, we discuss fingerprinting both distinct and identical smartphones and reach an accuracy close to 100% with several traditional classifiers.
Longest Common Subsequence-based Source Code Similarity

Determining the similarity of source codes is still a researched topic today. There are several ways to answer the question of whether two source codes are similar, and if so, how much. These methods can be divided into four major categories, one of which tries to process and compare the source codes in text form. In this paper, we present such a text-based approach based on the longest common subsequence. As our experimental results show a significant improvement in terms of similarity detection can be achieved with this method.
Convolutional-Reccurrent Neural Network Adaptation for Real-time Text Recognition on CPU

Many articles related to text recognition has been published in a few years. Most of them aim at achieving the highest recognition accuracy though never consider computational cost. While computational cost is highly important for wide use we will aim at low computational cost and minimum loss in recognition accuracy at the same time which can make real-time text recognition possible. We will describe several classical computer vision techniques which help reduce the computational cost, real-world dataset gathering, preprocessing and annotation, the process of architecture optimization and a few model quantization techniques. We will start with basic convolutional neural network architecture introduced in one of articles and step by step optimize it with quantization to be able to operate at real-time on central processing unit. Intermediate results will be compared on wide real-world dataset and different hardware setups. The result of the process described in the paper is model available to operate in real-time on low-end or mobile hardware and described image pre-processing algorithms.
Efficiency improvement of photon arrival time based quantum random number generator with hashing

Quantum random number generators can deliver entropy based on inherently non-deterministic physical phenomena, which is essential for applications where quality randomness is needed (e.g. in cryptography). Our generator based on photon time-of-arrival exploits the randomness of light emission in semiconductors. To measure the time between detections we use a continuous (non-restartable) clock for time tagging, which permits simpler hardware but produces slightly different output from the optimal exponential distribution. To handle this and produce quality output, we use Toeplitz hashing based bit generation after modeling and calculating a lower bound of extractable min-entropy for the general case of this generation scheme with a continuous clock. We produced uniformly distributed output bits with our setup, while also reaching a generation efficiency of 9.75 output bits per 16-bit input record, which is an improvement to our previously used method. Our generator has also been statistically tested using four of the popular statistical test suites.
Unsupervised Representation Learning in Multivariate Time Series with Simulated Data

Multivariate time-series data contains valuable information, but are challenging to analyze and model. This study uses unsupervised representation learning approaches to extract meaningful representations from unlabeled data, which are used to identify patterns of underlying states. These representations can be later utilized as inputs in downstream tasks for prognosis and health management (PHM) of complex physical systems, with the aim of quantifying system’s reliability and efficiency, and measuring the potential for failure, reducing downtime, and improving overall safety. We evaluate the performance of three advanced methods, namely Temporal Neighborhood Coding (TNC), Triplet Loss, and Contrastive Predictive Coding (CPC), on three simulated scenarios that mimic real-world situations. Key Performance Indicators are used as evaluation metrics for clustering and classification tasks. Our objective is to demonstrate the practicality of these approaches in multiple scenarios while remaining domain agnostic.
Motion planning and modeling for isothermal parallel chemical reactions

For several chemical reactions, the intermediate states of the system has to be known. They can be given, e.g., by sampling, measurements, or motion planning. By motion planning, we can determine the time function of the concentrations and the temperature, using the mathematical model of the system. From this, we can conclude the state of the system at every moment. We provide a motion planning method for isothermal parallel structural chemical reactions. We simulate the behavior of the three-step parallel reaction. The results of the motion planning can help to decide, if it is necessary to intervene from outside, thus we can ensure that the concentration is in a specified interval.
An Algorithm for Concurrent Use of Quantum Simulators and Computers in the Context of Subgraph Isomorphism

Quantum computers represent the new generation of computing. But despite this, they have a limit which consists of the number of qubits, or quantum bits, that each computer has at its disposal. The purpose of this research is the ability to use multiple quantum simulators and computers in order to achieve calculations made by a unified number of qubits. The design for this algorithm makes use of parallel and distributed programming. The key results are amplified amplitudes obtained for large quantum circuits that would otherwise be impossible to execute on a single quantum computer whose available number of qubits is exceeded by the one of such circuits. Thus the algorithm presented in the current paper has been designed and tested, showing favorable results. A 25 qubit circuit was used for the test cases. It was partitioned in multiple smaller circuits that were sent for execution from a laptop, directly to the quantum devices. The circuit number of qubits can be increased as considered necessary by the user.
Economic Impact of a Hospital Cyberattack in a National Health System: Descriptive Case Study

Over the last decade, the frequency and size of cyberattacks in the health care industry have increased, ranging from breaches of processes or networks to encryption of files that restrict access to data. These attacks may have multiple consequences for patient safety, as they can, for example, target electronic health records, access to critical information, and support for critical systems, thereby causing delays in hospital activities. The effects of cybersecurity breaches are not only a threat to patients' lives but also have financial consequences due to causing inactivity in health care systems. However, publicly available information on these incidents quantifying their impact is scarce.We aim, while using public domain data from Portugal, to (1) identify data breaches in the public national health system since 2017 and (2) measure the economic impact using a hypothesized scenario as a case study.We retrieved data from multiple national and local media sources on cybersecurity from 2017 until 2022 and built a timeline of attacks. In the absence of public information on cyberattacks, reported drops in activity were estimated using a hypothesized scenario for affected resources and percentages and duration of inactivity. Only direct costs were considered for estimates. Data for estimates were produced based on planned activity through the hospital contract program. We use sensitivity analysis to illustrate how a midlevel ransomware attack might impact health institutions' daily costs (inferring a potential range of values based on assumptions). Given the heterogeneity of our included parameters, we also provide a tool for users to distinguish such impacts of different attacks on institutions according to different contract programs, served population size, and proportion of inactivity.From 2017 to 2022, we were able to identify 6 incidents in Portuguese public hospitals using public domain data (there was 1 incident each year and 2 in 2018). Financial impacts were obtained from a cost point of view, where estimated values have a minimum-to-maximum range of €115,882.96 to €2,317,659.11 (a currency exchange rate of €1=US $1.0233 is applicable). Costs of this range and magnitude were inferred assuming different percentages of affected resources and with different numbers of working days while considering the costs of external consultation, hospitalization, and use of in- and outpatient clinics and emergency rooms, for a maximum of 5 working days.To enhance cybersecurity capabilities at hospitals, it is important to provide robust information to support decision-making. Our study provides valuable information and preliminary insights that can help health care organizations better understand the costs and risks associated with cyber threats and improve their cybersecurity strategies. Additionally, it demonstrates the importance of adopting effective preventive and reactive strategies, such as contingency plans, as well as enhanced investment in improving cybersecurity capabilities in this critical area while aiming to achieve cyber-resilience.
GPU Acceleration of Longest Common Substrings Algorithm

The Longest Common Substring of two strings is a character sequence that appears in both texts and is the longest of these. The method is widely used in several text similarity measurement methods, usually used multiple times on the same textual data. There are several already known methods to solve the problem, but these are mostly based on very time and memory intensive procedures. This paper presents a novel data-parallel model to solve the same problem, available for GPU implementation. As our experimental results show, the data-parallel implementation is significantly faster for long textual data.
Public Key Infrastructure in the Post-Quantum Era

In the last few years the threat of quantum computers has become bigger and bigger. The fast computing of the quantum computers puts everything in danger that relies on classical cryptographic algorithms. The question of when a large-scale quantum computer will be built cannot be exactly answered yet. According to the Cloud Security Alliance at around 2030 the power of quantum computers will increase so that the risk for exposure of traditionally encrypted data will be very high. PKI (Public Key Infrastructure) relies on asymmetric cryptographic algorithms and is widely used in many fields. So it is important that it will be completely safe. The way to provide that is to change traditional cryptographic algorithms to post-quantum cryptography algorithms or hybrid solutions. To this, we review in this paper the basics of classical PKI and quantum cryptography methods and the threat of quantum computing for PKI. We present quantum-safe solutions already in use and recommend new, well-performing solutions for a quantum-resistant PKI by comparison.
Examining the competences needed for an agile approach in different generations

Resilience, managing change appropriately and effectively, is crucial for both individuals and organisations in today’s world. This is one of the main reasons why the agile approach, which originated in the world of software development, is becoming more and more widespread, as it is precisely the ability to adapt to change that is one of its main strengths. However, the agility of organisations and the agile execution of projects is unthinkable without the agility of individuals, which is the result of a combination of different attributes, competences, skills and abilities. These may vary from generation to generation. The aim of our research is to answer the question of what are the most important human attributes for an agile approach and how these attributes vary between generations.
GITS: A Graph-Indexed-Tensor Structure for the Adaptive Associative-Semantic Tagging of Digital Documents

With the appearance of 2D graphical user interfaces in the 1980s, users began to carry out most operations on 2D icon-based interfaces instead of using line-based terminals. With the emergence of smartphones in the 2010s, the notion of portable 2D graphical user interfaces was born, and by today, users accessing digital services are no longer tethered to a single location. All of these advances have led to immense changes in our conceptualization of digital information systems, the effects of which are difficult to overestimate. Recent developments in virtual and augmented reality (VR/AR), as well as in Internet of Things (IoT) and artificial intelligence (AI) are poised to lead to the next major breakthrough in this series of cognitive expansions, bringing to the forefront portable, context-aware spatial interfaces. A consequence of these developments is that users are expecting to be able to access a growing multitude and variety of digital content in ways that are increasingly contextualized and personalized, i.e., relevant to the time, location and topic from the perspective of the users’ personal history. In this paper, we propose an adaptive content labeling and storage model that is suitable to these challenges. Our model, called the Graph-Indexed Tensor Store (GITS) has the benefits of being adaptive and personalized, while also allowing for content retrieval to be carried out based on associative search operations. In a preliminary analysis of the model, we address the challenges of what we refer to as syntactic, semantic and pragmatic saturation, and provide ways to quantity and further explore their effects.
Efficient Face Region Occlusion Repair Based on T-GANs

In the image restoration task, the generative adversarial network (GAN) demonstrates excellent performance. However, there remain significant challenges concerning the task of generative face region inpainting. Traditional model approaches are ineffective in maintaining global consistency among facial components and recovering fine facial details. To address this challenge, this study proposes a facial restoration generation network combined a transformer module and GAN to accurately detect the missing feature parts of the face and perform effective and fine-grained restoration generation. We validate the proposed model using different image quality evaluation methods and several open-source face datasets and experimentally demonstrate that our model outperforms other current state-of-the-art network models in terms of generated image quality and the coherent naturalness of facial features in face image restoration generation tasks.
Application of Ensembles of Neural Networks for Deepfake Recognition

At the moment, the creation of deepfakes for various activities is widespread: sometimes for the sake of humor, and sometimes for malicious purposes. In the second case, such deepfakes can potentially harm a person. Neural networks trained to recognize deepfakes can become an actual tool for combating malicious fakes. This article proposes neural network architectures which can be used in the task of recognizing photo and video deepfakes, describes the datasets that can help in training neural networks for the task, and determines what pre-processing is needed for different datasets. Particular attention is paid to possible options for combining the results of several trained models. The architectures of neural networks Inception-Resnet and Xception were chosen as the basis, the conceptual features of these architectures were described, and their practical testing was carried out in the problem of classifying deepfakes. A method of ensembling models is chosen for better results.
A Runtime-Efficient Multi-Object Tracking Approach for Automotive Perception Systems

There is an increasing demand for fully autonomous driving with the spread of advanced driver assistance systems. However, a higher level of automation requires an enhanced environment perception system. The automotive smart sensors detecting the surrounding objects are usually subject to various uncertainties. Objects are sometimes misdetected, false detections may also occur, and the sensor measurements are noisy. Multi-object tracking algorithms aim to compensate for these uncertainties, estimating precisely and continuously the state of surrounding objects. The real-time execution of these algorithms is crucial in automotive applications. This paper presents an improved runtime-efficient local nearest neighbor-based approach. The performance metrics of the proposed method get close to the state-of-the-art algorithms, besides having significantly lower computational complexity.
Volcanic earthquake catalog enhancement using integrated detection, matched-filtering, and relocation tools

Volcanic earthquake catalogs are an essential data product used to interpret subsurface volcanic activity and forecast eruptions. Advances in detection techniques (e.g., matched-filtering, machine learning) and relative relocation tools have improved catalog completeness and refined event locations. However, most volcano observatories have yet to incorporate these techniques into their catalog-building workflows. This is due in part to complexities in operationalizing, automating, and calibrating these techniques in a satisfactory way for disparate volcano networks and their varied seismicity. In an effort to streamline the integration of catalog-enhancing tools at the Alaska Volcano Observatory (AVO), we have integrated four popular open-source tools: REDPy, EQcorrscan, HypoDD, and GrowClust. The combination of these tools offers the capability of adding seismic event detections and relocating events in a single workflow. The workflow relies on a combination of standard triggering and cross-correlation clustering (REDPy) to consolidate representative templates used in matched-filtering (EQcorrscan). The templates and their detections are then relocated using the differential time methods provided by HypoDD and/or GrowClust. Our workflow also provides codes to incorporate campaign data at appropriate junctures, and calculate magnitude and frequency index for valid events. We apply this workflow to three datasets: the 2012–2013 seismic swarm sequence at Mammoth Mountain (California), the 2009 eruption of Redoubt Volcano (Alaska), and the 2006 eruption of Augustine Volcano (Alaska); and compare our results with previous studies at each volcano. In general, our workflow provides a significant increase in the number of events and improved locations, and we relate the event clusters and temporal progressions to relevant volcanic activity. We also discuss workflow implementation best practices, particularly in applying these tools to sparse volcano seismic networks. We envision that our workflow and the datasets presented here will be useful for detailed volcano analyses in monitoring and research efforts.
Performance Evaluation of the Rockchip Systems-on-Chip Through YOLOv4 Object Detection Model

This paper provides performance research of the Rockchip systems-on-chip RK3568 and RK3588 through convolutional neural network YOLOv4 in terms of average inference time and average power consumption. The obtained values were compared with the performance of NVIDIA Jetson Nano through the same neural network. We also compared precision, recall and F-score between half-precision model and asymmetric quantized model. Average inference time of the quantized YOLOv4 is 149 ms on Rockchip RK3588 at 8.9W power consumption, 481 ms - on Rockchip RK3568 at 5.5W power consumption. Rockchip RK3588 performs YOLOv4 half-precision model 1.3 times faster and Rockchip RK3568 performs the model 2.9 times slower than NVIDIA Jetson Nano. Average inference time of the quantized YOLOv4 is 361 ms on Rockchip RK3588, 1385 ms - on Rockchip RK3568. F-score of asymmetric quantized model detecting person equals 0.718 while percision is 1% higher and recall is 5% less compared to half-precision model. Average Intersection over Union metrics of quantized model equals 0.75 which is 8.5% less than that of half-precision model.
Model Organized Theoretical and Experimental Research in Collaborative Space

Recent advancements in high automated engineering essentially changed the requirements against and technical and organizational scenario around engineering related research. These changes came mainly from contextual integration, moving activities and content items into cloud organized virtual space, integration of theory, practice and experience, introduction of the three paradigm research, system level global problem solving, and all-in-one research environment. This paper introduces new research concept, methodology and laboratory technology to fulfill the above requirements and establish integration of aspects, collaboration and communication in high level information technology supported and research dedicated collaborative space. This new scientific-industrial research organizes content items in experimental model that has generic, contextual, reactive, and autonomous characteristics. The Virtual Research Laboratory (VRL) was founded for the implementation of the above research and it is now available to start with PhD student projects. VRL applies high end definition and management capabilities of the 3DEXPERIENCE engineering platform in cloud providing renewable virtual technology and all cloud resources as service from professionally platform management source. Possibility worldwide participation in collaborative space of research and connection this space to related collaborative spaces give chance for VRL participants to join world level PhD research projects and work on actual problems. In this way, authentic global assistance can be available in the collaborative space. This paper reveals requirements and solutions in program of the VRL, introduces the proposed collaborative space and experimental model organized research. Following this, practice of organizing research at VRL applying capabilities of cloud serviced engineering modeling platform (EMP) is introduced. Finally, challenges at starting PhD research program at VRL are discussed.