Reviewerly/exHarmony huggingface dataset:
Evolution of the Automotive Reference Architecture Model towards a Domain-Specific Systems Engineering Approach

Due to the rising complexity in the automotive domain a suitable modeling framework to facilitate development as well as interdisciplinary communication is needed. In the domain of smart grids, the Smart Grid Architecture Model (SGAM) framework turned out to be a suitable tool for coping with complexity whilst providing cross-disciplinary understanding. A similar approach exists for the automotive domain: the Automotive Reference Architecture Model (ARAM) framework. As opposed to other automotive frameworks, this one offers interoperability with the smart grid framework, which simplifies cross-domain collaboration. During the development of ARAM, the Software Platform Embedded Systems (SPES) methodology gained momentum. This methodology can be considered an architectural model while the automotive framework can be considered an analysis model, according to the domain-specific systems engineering approach. In order to follow this approach, a transformation between the two models must be possible. This work-in-progress paper aims at presenting first approaches of an adapted version of the ARAM framework, proposing a reworked structure that is compatible with both, SGAM and SPES.
Identifying Breast Cancer Cell Lines Using High Performance Machine Learning Methods

We present a computational framework based on machine learning classifiers K-Nearest Neighbors and Neighborhood Component analysis for breast cancer (BC) subtypes prognostic. Our results has up to 97% accuracy for prognostic stratification of BC subtypes.
Augmenting Deep Classifiers with Polynomial Neural Networks

Deep neural networks have been the driving force behind the success in classification tasks, e.g., object and audio recognition. Impressive results and generalization have been achieved by a variety of recently proposed architectures, the majority of which are seemingly disconnected. In this work, we cast the study of deep classifiers under a unifying framework. In particular, we express state-of-the-art architectures (e.g., residual and non-local networks) in the form of different degree polynomials of the input. Our framework provides insights on the inductive biases of each model and enables natural extensions building upon their polynomial nature. The efficacy of the proposed models is evaluated on standard image and audio classification benchmarks. The expressivity of the proposed models is highlighted both in terms of increased model performance as well as model compression. Lastly, the extensions allowed by this taxonomy showcase benefits in the presence of limited data and long-tailed data distributions. We expect this taxonomy to provide links between existing domain-specific architectures. The source code is available at https://github.com/grigorisg9gr/polynomials-for-augmenting-NNs .
Telepresence Video Quality Assessment

Video conferencing, which includes both video and audio content, has contributed to dramatic increases in Internet traffic, as the COVID-19 pandemic forced millions of people to work and learn from home. Global Internet traffic of video conferencing has dramatically increased Because of this, efficient and accurate video quality tools are needed to monitor and perceptually optimize telepresence traffic streamed via Zoom, Webex, Meet, etc.. However, existing models are limited in their prediction capabilities on multi-modal, live streaming telepresence content. Here we address the significant challenges of Telepresence Video Quality Assessment (TVQA) in several ways. First, we mitigated the dearth of subjectively labeled data by collecting $$\sim $$ 2k telepresence videos from different countries, on which we crowdsourced $$\sim $$ 80k subjective quality labels. Using this new resource, we created a first-of-a-kind online video quality prediction framework for live streaming, using a multi-modal learning framework with separate pathways to compute visual and audio quality predictions. Our all-in-one model is able to provide accurate quality predictions at the patch, frame, clip, and audiovisual levels. Our model achieves state-of-the-art performance on both existing quality databases and our new TVQA database, at a considerably lower computational expense, making it an attractive solution for mobile and embedded systems.
Efficient Learning of Hyperrectangular Invariant Sets Using Gaussian Processes

We present a method for efficiently computing reachable sets and forward invariant sets for continuous-time systems with dynamics that include unknown components. Our main assumption is that, given any hyperrectangle of states, lower and upper bounds for the unknown components are available. With this assumption, the theory of mixed monotone systems allows us to formulate an efficient method for computing a hyperrectangular set that over-approximates the reachable set of the system. We then show a related approach that leads to sufficient conditions for identifying hyperrectangular sets that are forward invariant for the dynamics. We additionally show that set estimates tighten as the bounds on the unknown behavior tighten. Finally, we derive a method for satisfying our main assumption by modeling the unknown components as state-dependent Gaussian processes, providing bounds that are correct with high probability. A key benefit of our approach is to enable tractable computations for systems up to moderately high dimension that are subject to low dimensional uncertainty modeled as Gaussian processes, a class of systems that often appears in practice. We demonstrate our results on several examples, including a case study of a planar multirotor aerial vehicle.
Theoretical model of artistic and project-based experience of future teachers of Musical Arts

The article presents the results related to theoretical modelling of the phenomenon “artistic and project-based experience” of future teachers of Musical Arts and the criteria for its evaluation. The formation of this type of experience on the basis of piano training aimed at future teachers of Musical Arts is actualized by the fact that projects become the most attractive innovative organisational form of teaching arts. Researchers explore various phenomena related to project-making (competence, skills, technology, activities, culture, etc.). However, artistic and project-based experience in the context of professional training aimed at future teachers of Musical Arts still needs theoretical understanding. The article clarifies the essence of this type of experience; the corresponding concept is interpreted as an integrated professional quality of future Musical Arts teachers; it is a process and a result of accumulation and memorisation of knowledge and practical skills enabling the embodiment of artistic and essence-related ideas into an organisational form perceived as an event where all participants are involved into independent preparing and performing of distributed creative tasks according to a certain scenario, focusing on achieving a common pedagogical goal. The structure of artistic and project-based experience is presented; it consists of four components: methodologically motivated component (aspiration to acquire methodical competence in piano performance; an interested attitude to mastering piano performance as a pedagogical resource of professional training); artistic and mental (the presence of the memory fund of artistic and project-related events and their reflection (artistic and project-based thinking)); activity-based and communicative (the presence of experience of artistic and communicative actions; activity in mastering the artistic and communicative resource of piano training); creative and self-organisational (experience of self-organisation in pedagogical project-making related to piano training; integrative and praxeological resources of the experience when dealing with artistic and figurative project-making and script-planning of events). The assessment criteria and indicators of the developed components are presented: motivational and intentional; information-based and operational; technological and innovative; productive and creative.
Evaluation of grid-connected photovoltaic system in omani harsh weathers CO2

Recently, a clear tendency has emerged for the Omani government to spread the culture of reliance on renewable energies in the production of electricity in the country. One of the most important aspects of this trend is that the Omani government has installed PV systems connected to the national grid in some schools across the country. The current study aimed at assessing and practically measuring the performance of the photovoltaic power plant installed in one of these schools (Asma Bint Al-Hareth School in Sohar, Oman) over a whole year. This system has a maximum power of 150 kW. The practical measurements for a whole year of the station’s output showed that the system losses varied from low values to high values. The results showed that a clear variation occurs in the outputs of the system as a result of changing seasons and weather. The practical results demonstrated the suitability of Oman’s environmental conditions for the deployment and operation of such stations connected to the grid.
Mobile-based Patient-centric EMR Sharing System Using Blockchain

Nowadays, mobile devices are the most important components in human life. With the help of various apps, we interact with other people, play games to kill time, or perform online shopping activities. However, despite the convenience of mobile devices, most cross-institutional EMR sharing still needs written forms and waiting time for application. Although existing blockchain-based data exchange architectures have been proposed, the most frequently used mobile devices have never been considered. This study is carried out to integrate mobile devices to develop a blockchain-based patient-centric EMR sharing system considering data security, data integrity, and patient privacy. Under the patient's consent, the proposed system supports selective data sharing between different institutions through blockchain in real-time. Patients have the complete right to disclose what their EMR shares. Further, using mobile devices to scan the QR code provided by the doctor, patients easily employ this public key infrastructure to achieve enhanced data security. The proposed system provides necessary functionalities (authentication, integrity, and access control) for EMR data exchange. It shows how mobile features (camera, notification, bio-authentication) can be favorable to fulfill patient satisfaction.
Bitcoin Investment Strategies Based on Google Trends and AI Models

The evolution of the price of bitcoin has captured the attention of analysts in recent years. But how can a cryptocurrency be valued? Given that the price is linked to expectations, we propose, in this paper, to predict the trend of bitcoin using Google Trends as an explanatory variable. To do so, we develop two alternative algorithmic trading systems that buy or sell bitcoin depending on whether the searches for this term in Google increase or decrease. The approach is powered using artificial intelligence. The results of these trading systems are positive and show that trading strategies can be implemented based on investors’ mood about an asset, in this case measured through Google Trends. The use of artificial intelligence in trading is new and this is an example of its potential.
ViP: Unified Certified Detection and Recovery for Patch Attack with Vision Transformers

Patch attack, which introduces a perceptible but localized change to the input image, has gained significant momentum in recent years. In this paper, we present a unified framework to analyze certified patch defense tasks, including both certified detection and certified recovery, leveraging the recently emerged Vision Transformers (ViTs). In addition to the existing patch defense setting where only one patch is considered, we provide the very first study on developing certified detection against the dual patch attack, in which the attacker is allowed to adversarially manipulate pixels in two different regions. By building upon the latest progress in self-supervised ViTs with masked image modeling (i.e., masked autoencoder (MAE)), our method achieves state-of-the-art performance in both certified detection and certified recovery of adversarial patches. Regarding certified detection, we improve the performance by up to $$\sim 16\%$$ on ImageNet without training on a single adversarial patch, and for the first time, can also tackle the more challenging dual patch setting. Our method largely closes the gap between detection-based certified robustness and clean image accuracy. Regarding certified recovery, our approach improves certified accuracy by $$\sim 2\%$$ on ImageNet across all attack sizes, attaining the new state-of-the-art performance.
Efficient FPGA-based ECDSA Verification Engine for Permissioned Blockchains

Permissioned blockchain platforms heavily depend on cryptography to provide a layer of trust within the blockchain network, thus verification of cryptographic signatures often becomes the bottleneck. ECDSA is the most commonly used cryptographic scheme in permissioned blockchains. In this work, we propose an efficient implementation of ECDSA signature verification on FPGA, in order to improve performance of permissioned blockchains that aim to use FPGA-based hardware accelerators. We propose several optimizations for modular arithmetic (e.g., custom multipliers and fast modular reduction) and point arithmetic (e.g., reduced number of point double and addition operations, and optimal width NAF representation). Based on these optimized modular and point arithmetic modules, we propose an ECDSA verification engine that can be used by any application for fast verification of signatures. We further optimize our ECDSA verification engine for Hyperledger Fabric (one of the most widely used blockchain platforms) by moving carefully selected operations to a precomputation block, thus simplifying the critical path of ECDSA signature verification. Our ECDSA engine running at 250MHz on Xilinx Alveo U250 accelerator card can perform a verification in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$760\mu s$</tex> with a throughput of 1, 315 verifications/sec, which is <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\sim 2.5$</tex> times faster than state-of-the-art FPGA-based implementations. Our Hyperledger Fabric-specific ECDSA engine can perform a verification in <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$368\mu s$</tex> , and 2, 717 verifications/sec. With 10 engines, Hyperledger Fabric can achieve a throughput of 7, 520 transactions/sec.
Digitally Enhanced Personality

The evolution of mankind is based on the expansion of the capabilities of Homo sapiens based on the development and mastery of technology as cultural tools for development. A letter and a book are prime examples. Today the human personality has been expanded not only with a pen, a watch, a spyglass and an encyclopedia but also with a calculator, an automatic translator, a digital navigator, access to the World Wide Web and other digital means of expanding the mind which tremendously increase the power of the human brain. We evaluate graduates in terms of their ability to engage in cognitive, labor, and other activities as an extended personality who masters digital means and is able to make a moral choice in a digital society. The system of educational goals, planned results, standards, programs should be addressed specifically to the extended personality of the student and teacher.
Stability-Guided Particle Swarm Optimization

Particle swarm optimization (PSO) performance has been shown to be sensitive to control parameter values. To obtain best possible results, control parameter tuning or self-adaptive PSO implementations are necessary. Theoretical stability analyses have produced stability conditions on the PSO control parameters to guarantee that an equilibrium state is reached. Should control parameter values be chosen to satisfy a stability condition, divergent and cyclic search behaviour is prevented, and particles are guaranteed to stop moving. This paper proposes that control parameter values be randomly sampled to satisfy a given stability condition, removing the need for control parameter tuning. Empirical results show that the resulting stability-guided PSO performs competitively to a PSO with tuned control parameter values.
Quantum Approximate Optimization Algorithm with Sparsified Phase Operator

The Quantum Approximate Optimization Algorithm (QAOA) is a promising candidate algorithm for demonstrating quantum advantage in optimization using near-term quantum computers. However, QAOA has high requirements on gate fidelity due to the need to encode the objective function in the phase separating operator, requiring a large number of gates that potentially do not match the hardware connectivity. Using the MaxCut problem as the target, we demonstrate numerically that an easier way to implement an alternative phase operator can be used in lieu of the phase operator encoding the objective function, as long as the ground state is the same. We observe that if the ground state energy is not preserved, the approximation ratio obtained by QAOA with such phase separating operator is likely to decrease. Moreover, we show that a better alignment of the low energy subspace of the alternative operator leads to better performance. Leveraging these observations, we propose a sparsification strategy that reduces the resource requirements of QAOA. We also compare our sparsification strategy with some other classical graph sparsification methods, and demonstrate the efficacy of our approach.
A Hybrid Quantum Computer for Quantum Finance Computation of Monte-Carlo Distribution

A quantum generative adversarial network with the genetic algorithm is demonstrated for quantum finance distribution loading. The hybrid quantum computer implemented by a fabricated quantum photonic chip shows its capability of generating distribution precisely.
A Comparative Study of Ant Colony Optimization and Genetic Algorithm for Path Planning of Aerial Food Delivery System

As technology rules the world, drones are used for creative and valuable applications. Apart from the military applications, the usage of drones in civil domains has gained immense popularity in recent days. Emerging and re-emerging viral pathogens continue to pose a major threat to global public health. As one of the prevention method is to maintain a safe distance from others, contact less delivery using drones becomes more relevant. This paper concentrates on the parcel delivery system using drones. Unmanned Aerial Vehicles can replace the conventional manual food delivery system effectively while consuming less time. The main challenge for parcel delivery drones is to plan the optimal path meeting several constraints. In the current work, the path planning of delivery drones is formulated as a travelling sales man problem and solved with two different algorithms-Ant Colony Optimization and Genetic Algorithm. Comparison is done for analyzing the efficiency and performances of both algorithms in the case of aerial food delivery system.
Debugging a Policy: Automatic Action-Policy Testing in AI Planning

Testing is a promising way to gain trust in neural action policies π. Previous work on policy testing in sequential decision making targeted environment behavior leading to failure conditions. But if the failure is unavoidable given that behavior, then π is not actually to blame. For a situation to qualify as a "bug" in π, there must be an alternative policy π' that does better. We introduce a generic policy testing framework based on that intuition. This raises the bug confirmation problem, deciding whether or not a state is a bug. We analyze the use of optimistic and pessimistic bounds for the design of test oracles approximating that problem. We contribute an implementation of our framework in classical planning, experimenting with several test oracles and with random-walk methods generating test states biased to poor policy performance and/or state novelty. We evaluate these techniques on policies π learned with ASNets. We find that they are able to effectively identify bugs in these π, and that our random-walk biases improve over uninformed baselines.
Audio-Visual Mismatch-Aware Video Retrieval via Association and Adjustment

Retrieving desired videos using natural language queries has attracted increasing attention in research and industry fields as a huge number of videos appear on the internet. Some existing methods attempted to address this video retrieval problem by exploiting multi-modal information, especially audio-visual data of videos. However, many videos often have mismatched visual and audio cues for several reasons including background music, noise, and even missing sound. Therefore, the naive fusion of such mismatched visual and audio cues can negatively affect the semantic embedding of video scenes. Mismatch condition can be categorized into two cases: (i) Audio itself does not exist (ii) Audio exists but does not match with visual. To deal with (i), we introduce audio-visual associative memory (AVA-Memory) to associate audio cues even from videos without audio data. The associated audio cues can guide the video embedding feature to be aware of audio information even in the missing audio condition. To address (ii), we propose audio embedding adjustment by considering the degree of matching between visual and audio data. In this procedure, constructed AVA-Memory enables to figure out how well the visual and audio in the video are matched and to adjust the weighting between actual audio and associated audio. Experimental results show that the proposed method outperforms other state-of-the-art video retrieval methods. Further, we validate the effectiveness of the proposed network designs with ablation studies and analyses.
LoPF: An Online LiDAR-Only Person-Following Framework

This article presents an online light detection and ranging (LiDAR)-only person-following (LoPF) framework, which can be applied in both indoor and outdoor environments with only sparse LiDAR point cloud data as input. The framework consists of two cascade backbone procedures. The former is pedestrian detection, which is developed for classifying a set of human clusters from noisy and sparse LiDAR point clouds. An originally designed voxel feature is integrated for depicting the 3-D information of each potential cluster. A U-shaped convolutional neural network (CNN) is proposed to achieve human cluster detection. Then the latter procedure, pedestrian tracking, is developed to follow the target. Specifically, to achieve long-term person-following, an unscented Kalman filter (UKF), a position filter, and a target reidentification module are combined. The reidentification module contains a specifically designed support vector machine (SVM) target classifier. The developed framework is verified by extensive real-world experiments in outdoor environments using mobile robots. Compared with the state of the art, our method is better in target identification and tracking, realizing better performance in the person-following.
Meliorated Single Image Dehazing using Dark Channel Prior with Histogram Equalization

Haze is one of the bad weather conditions due to which the visual nature of the picture is defoliated. The previously existing methodologies used various removal systems, including Fuzzy logic, ANN, Dark channel prior where the computational complexity is very high and also the reliability of the process is very low. This paper proposed a fusion of the dark channel earlier technique with Histogram Equalization to correct contrast and also calculate the performance by evaluating the PSNR, MSE and ENTROPY values to improve the reliability and reduce the computational complexity.