Reviewerly/exHarmony huggingface dataset:
Evaluation and technological solutions for a dynamic, unified cloud programming development environment : Ease of use and applicable system for uniformized practices and assessments

The dynamic development and change of the world also result in the modernization of teacher-student relationships. In the training, educational processes must be planned with innovative strategies that meet the increasingly changing needs of students, which today go beyond the traditional classroom and methodological frameworks. In this article, we present a technological solution to support the renewal efforts in the education world that, on the one hand, takes advantage of the benefits of cloud-based services, and on the other hand, ensures that students can acquire the material of a given course in a unified, stable environment. The isolated environment offered by the virtual space guarantees private work sessions, and multi-project activities can also be handled more easily and uniformly. The analysis of the teaching program presented below provides insight into the practical and appropriate solutions offered by the containerization process. In addition to these, the topic will also cover the widely used and popular Docker application that supports the implementation of the process, as well as the NodeJS environment closely related to the project.
A Survey of Usage of Anytime Algorithm in Fault detection in Cloud Systems

Fault detection, which requires a lot of time and complexity, is one of the most difficult tasks for cloud computing. In this research, we investigate the utilization of the anytime algorithm in cloud-based fault detection techniques. Anytime algorithms are real-time decision-making algorithms that focus on time-deliberation. In this paper, the basic concepts of the Anytime algorithm in existing works as well as its benefits of usage are analyzed. Various literature is reviewed and combined for possible fault recognition and time deliberation. The paper includes details of anytime processing in fault detection in different subject areas. Based on the survey, we aim to define the algorithm processing implementation as future work.
Comparison of neural architectures for machine translation of the Slovak language using the Fairseq toolkit

In the article, we describe comparison of different architectures for machine translation of the Slovak language using the Fairseq toolkit. The article describes current trends and the most used architectures (e.g. convolutional neural network, Long short-term memory, recurrent neural network and Transformer) in connection with machine translation. We also focus on the issue of parallel corpus and its use in connection with machine translation. We have created a short overview of the issue, we described our experimental results, and suggested possible solutions to problems in machine translation field for Slovak language.
Extensible Hardware Inference Accelerator for FPGA using Models from TensorFlow Lite

Neural Networks (NN) are powerful tools for solving complex problems. In recent years, its use has been increasing in several areas. However, the inference of NN has a high computational demand, requiring hardware accelerators. Properly distributing the computational load across hardware and software resources is key to improve performance and low energy consumption. This paper presents a hardware-software design to accelerate the inference of NN by using an eXtensible Neural Processing Unit (XNPU). For doing so, we used TensorFlow Lite to adapt the 8-bit integer parameter models to an XNPU architecture implemented at Field Programmable Gate Array. The results showed that using XNPU to NN inference can be promising due to obtained levels on performance and energy consumption. However, its use require some adaptions, particularly in quantized Node equation, aiming to avoid accuracy loss.
ABCAS: Adaptive Bound Control of spectral norm as Automatic Stabilizer

Spectral Normalization is one of the best methods for stabilizing the training of Generative Adversarial Network. Spectral Normalization limits the gradient of discriminator between the distribution between real data and fake data. However, even with this normalization, GAN's training sometimes fails. In this paper, we reveal that more severe restriction is sometimes needed depending on the training dataset, then we propose a novel stabilizer which offers an adaptive normalization method, called ABCAS. Our method decides discriminator's Lipschitz constant adaptively, by checking the distance of distributions of real and fake data. Our method improves the stability of the training of Generative Adversarial Network and achieved better Fréchet Inception Distance score of generated images. We also investigated suitable spectral norm for three datasets. We show the result as an ablation study.
Monocular 3D Object Detection of Moving Objects Using Random Sampling and Deep Layer Aggregation

Restoring 3D information from a single 2D image is an ill-posed problem, and accurate depth estimation is challenging. We propose a novel method that estimates depth more accurately by randomly sampling object-wise several additional points, otherwise, conventional methods mostly estimate the depth with only one center point. To sample object-wise multiple reference points, we generate sampling space, and we obtain the ground truth of several sampling points by moving the sampled points from the sampling space to the object space. Using the obtained ground truth, the depth of reference points can be obtained through the proposed reference points depth branch. The proposed network achieved better performance by allowing depth to be estimated densely rather than the conventional network estimating the depth sparsely.
360DIV: 360° Video Plus Depth for Fully Immersive VR Experiences

360° cameras can easily capture events and let viewers across the world re-live them in Virtual Reality (VR). However, the viewer can only rotate their head and there is no correct sense of depth, breaking the immersion. This work describes a framework that reconstructs the captured scene in 3D from a 360° image/video. To do this, it requires depth information, either from depth estimation or depth sensors. The reconstruction is rendered in VR in real-time. Areas of the scene that were not captured, are inpainted in a fast and non-intrusive way. A demo is made available online and will be used for user experiments to assess the perceived quality of experience.
Adaptive Chunk Grouping Method for Low-Latency HTTP-Based Live Streaming

The common media application format (CMAF) is an HTTP-based live streaming standard that enable low-latency streaming. However, exisiting bandwidth estimation method for adaptive bit rate is not accurate due to that the download time includes not only network bandwidth but also the idle times between chunks in a segment. In this paper, we propose an extended chunk-grouping method for low-latency HTTP-based live streaming. The proposed method adaptively changes the time to delay a request based on the risk of re-buffering. We show that the proposed method can significantly increase the average bit rate at the expense of minimal rebuffering time.
Encoder-Decoder based Segmentation Model for UAV Street Scene Images

Global contextual information needs to be modeled precisely for accurate segmentation of images taken by Unmanned Aerial Vehicles (UAVs). This paper presents a transformer-based method for UAV street scene semantic segmentation. The method uses an encoder-decoder-based architecture to capture local and global context information in UAV images. Experimental result of the proposed method shows competitive performance against state-of-the-art methods by achieving mIoU of 61.93% on UAVid dataset.
A Quasi-Initial Solution Giving Method for Ising Machines by Controlling External Magnetic Field Coefficients

Ising machines can find optimum or quasi-optimum solutions of combinatorial optimization problems efficiently and effectively. It is known that, when a good initial solution is given to an Ising machine, we can finally obtain a solution closer to the optimal solution. However, several Ising machines cannot directly accept an initial solution due to its computational nature. In this paper, we propose a method to give quasi-initial solutions into Ising machines that cannot directly accept them. The proposed method gives the positive or negative external magnetic field coefficients (magnetic field controlling term) based on the initial solutions and obtains a solution by using an Ising machine. Then, the magnetic field controlling term is re-calculated every time an Ising machine repeats the annealing process, and hence the solution is repeatedly improved on the basis of the previously obtained solution. The proposed method is applied to the ca-pacitated vehicle routing problem with an additional constraint (constrained CVRP) and the max-cut problem. Experimental results show that the total path distance is reduced by 6.74 % on average compared to the initial solution in the constrained CVRP and the sum of cut-edge weight is increased by 1.17% on average in the max-cut problem.
Investigating Suitability of Inverse Tone Mapping for Medical Images

Standard dynamic range (SDR) technology has been the foundation of medical imaging to this day, making medical images lack contrast and details. To address this issue, a feasible option before adopting high dynamic range (HDR) technology in the medical image capturing is to use inverse tone mapping (iTMO) to convert SDR images into HDR images. This approach can synthetically recover some of the information lost during the SDR capturing process and increase the contrast and overall brightness of the image. This paper is the first study that evaluates the performance of existing famous iTMOs for the medical images. In our study we used Breast Cancer Biopsy whole slide images as an example type of medical images. Our performance evaluations showed that some iTMOs achieved a significant improvement in visual quality, offering a better contrast and details, and the potential to help the accuracy at diagnosis phases.
Porting the Paillier Algorithm for Homomorphic Encryption on Portable Devices

Cloud computing is a widely adopted technology that offers storage, collaboration tools, and processing power to users. In many cases, data are originated from portable devices, that are built to improve our quality of life by sending sensitive personal data, e.g., health data to doctors or cloud platforms. However, saving and transferring huge volumes of data that contain sensitive information raises many privacy concerns. Homomorphic Encryption is a form of Encryption that can perform computation on encrypted data without the need of decrypting it, giving an answer to security issues. This paper focuses on the implementing of an Homomorphic scheme (i.e. Paillier) at the hardware level and specifically a PYNQ-Z2, alowcost FPGA board, to make Encryption more efficient. The results are compared to software implementations for the PYNQ-Z2 and to a powerful i7 processor. Comparing the execution times, reduction is achieved in the range from 19.4% to 27.3%, for a number of processes of the Paillier algorithm.
Signing in the Air by Pinching Multi-Fingers for Advanced Authentication on Embedded System

The behavior of air signature does not require paper and pen. However, in the absence of visual feedback and stroke order information, it increases the difficulty of identifying the trajectory of air signatures. At the same time, the air signature is more difficult to forge. This paper proposes a novel, contactless and advanced authentication method. When the user signs in the air, we use the camera to capture the image data. Furthermore, MediaPipe is adopted on an embedded system to capture finger trajectories. The user uses 4 different pinching fingers methods as the pen action for starting to sign. Different combinations of the 4 different pinching fingers represent a password combination. Therefore, the trajectory of the signature in the air has the characteristics of a password. In this paper, the dynamic time warping (DTW) algorithm is used to compare the similarity between the current signature and the registered signature. Experiments show that the in-the-air signature mechanism of the implicit password through the combination of pinching fingers can effectively and quickly reject forged signature in the early stage of verification. In addition, this paper has the execution performance of 30 frame per second (fps) on the Jetson Xavier NX developed by Nvidia, which has the commercial value of low power consumption and low cost to meet the needs of the consumer market.
Split Federated Learning and Reinforcement based Codec Switching in Edge Platform

In recent times, Split Federated Learning (SFL) is being proposed in the domain of Artificial Intelligence (AI) & Machine Learning (ML), where AI/ML models are partitioned into two or more sub-networks among clients and servers (i.e., edge/cloud). However, in the SFL approach, it is not clear on what basis the AI/ML model is partitioned among the client and the edge. In a partial offload scenario, to mitigate this issue, we propose an Optimal Split Federated Learning (O-SFL) mechanism that finds an optimal split of a DNN model based on network bandwidth where no media data is transferred but only partial output of the model is shared among clients and the edge device. However, in full offload scenario, where media data is transferred from clients to edge, it is possible that the codec that is being currently used for encoding frames is not suitable due to the current network bandwidth fluctuations. To solve this issue, we propose a Reinforcement Learning based Codec Switching (RLCS) mechanism that provides a-priori detection of a suitable codec based on current networks bandwidth conditions. We perform simulations to compare the performance of the O-SFL which provides significant improvements over SFL (considering various split points) for total training time tested with Wi-Fi and LTE network (as 5G network is not available currently). We also show the performance of RLCS mechanism with respect to the traditional fixed video codec mechanism.
Mixed Reality-Enabled Multilateral Collaboration Application Platform with AI and IoT Convergence

Internet of Things (IoT), Artificial Intelligence (AI), and Mixed Reality (MR) are recognized as ones of the most promising core technologies to lead the 4th industrial revolution. Converging MR with IoT and AI helps recognize situations by displaying the contents of analyzed data obtained from IoT servers on HMD. In this paper, we introduce the MR-enabled Convergence Platform with AI and IoT, called MR-IoT/AI Convergence Platform, shortly MIAcP, and MR-enabled multilateral collaboration application platform as an example of MIAcP. Then, we present the use cases of the applications that can apply to various fields such as disaster response, military surveillance, industrial digital twins, themed entertainments, and others.
Multi-path Data Transmission to Protect Data in Transit

In the last few decades, society has seen rapid advancements in Information and Communications Technology (ICT), which have revolutionized how data is created, transmitted, stored, and exchanged. ICT is a vast and everchanging subject, but two important concepts derived from ICT are data communications and networking. Data communications involve the use of a transmission medium, wired or wireless, to allow for the exchange of data between two or more devices. Many of the applications people rely on today require the use of data, including personal data, and thus, are enabled by data communications. Increased efficiency and speed of data communication have seen paralleled increases in the field of communication surveillance. When it comes to data transmission, surveillance is of the utmost importance. However, how data during transmission is protected is a topic that requires endless exploration. This paper begins with a discussion of ICT and how it has evolved within society today. Next, surveillance techniques for data transmission are explored, followed by counter-surveillance techniques to deter potential hackers from accessing private data. During the discussion of counter-surveillance, a novel counter-surveillance technique is proposed. This technique centers around directing traffic from the transmitting side over multiple links to the receiving side, resulting in data aggregation. Utilizing a powerful packet manipulation tool, Scapy, along with raw socket programming using the Python programming language, this paper presents, implements, tests, and evaluates a proof of concept for the proposed system. Using a stable Wi-Fi connection and a mobile hotspot as the two-multimedia links, with segments being sent in on alternating links, secure and successful data transmission from the client side to the server side was achieved.
Overparameterized Linear Regression Under Adversarial Attacks

We study the error of linear regression in the face of adversarial attacks. In this framework, an adversary changes the input to the regression model in order to maximize the prediction error. We provide bounds on the prediction error in the presence of an adversary as a function of the parameter norm and the error in the absence of such an adversary. We show how these bounds make it possible to study the adversarial error using analysis from non-adversarial setups. The obtained results shed light on the robustness of overparameterized linear models to adversarial attacks. Adding features might be either a source of additional robustness or brittleness. On the one hand, we use asymptotic results to illustrate how double-descent curves can be obtained for the adversarial error. On the other hand, we derive conditions under which the adversarial error can grow to infinity as more features are added, while at the same time, the test error goes to zero. We show this behavior is caused by the fact that the norm of the parameter vector grows with the number of features. It is also established that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _\infty$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> -adversarial attacks might behave fundamentally differently due to how the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{1}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\ell _{2}$</tex-math></inline-formula> -norms of random projections concentrate. We also show how our reformulation allows for solving adversarial training as a convex optimization problem. This fact is then exploited to establish similarities between adversarial training and parameter-shrinking methods and to study how the training might affect the robustness of the estimated models.
Knowledge Distillation Hashing for Occluded Face Retrieval

Deep hashing has proven to be efficient and effective for large-scale face retrieval. However, existing hashing methods are designed for normal face images only. They fail to consider the fact that face images may be occluded because of wearing masks, hats, glasses, etc. Retrieval performance of existing face retrieval methods is much worse when dealing with occluded face images. In this work, we propose the knowledge distillation hashing (KDH) to deal with occluded face images. The KDH is a two-stage learning approach with teacher-student model distillation. We first train a teacher hashing network using normal face images and then the knowledge from teacher model is used to guide the optimization of the student model using occluded face images as input only. With knowledge distillation, we build a connection between imperfect face information and the optimal hash codes. Experimental results show that the KDH yields significant improvements and better retrieval performance in comparison to existing state-of-the-art deep hashing retrieval methods under six different face occlusion situations.
A spawning particle filter for defocused moving target detection in GNSS-based passive radar

Global Navigation Satellite System (GNSS)-based passive radar (GBPR) has been widely used in remote sensing applications.However, for moving target detection (MTD), the quadratic phase error (QPE) introduced by the non-cooperative target motion is usually difficult to be compensated, as the low power level of the GBPR echo signal renders the estimation of the Doppler rate less effective.Consequently, the moving target in GBPR image is usually defocused, which aggravates the difficulty of target detection even further.In this paper, a spawning particle filter (SPF) is proposed for defocused MTD.Firstly, the measurement model and the likelihood ratio function (LRF) of the defocused point-like target image are deduced.Then, a spawning particle set is generated for subsequent target detection, with reference to traditional particles in particle filter (PF) as their parent.After that, based on the PF estimator, the SPF algorithm and its sequential Monte Carlo (SMC) implementation are proposed with a novel amplitude estimation method to decrease the target state dimension.Finally, the effectiveness of the proposed SPF is demonstrated by numerical simulations and preliminary experimental results, showing that the target range and Doppler can be estimated accurately.
Tackling Over-Smoothing in Graph Convolutional Networks With EM-Based Joint Topology Optimization and Node Classification

Over-smoothing has emerged as a severe obstacle to node classification with message passing based graph convolutional networks (GCNs). Classification performance dramatically deteriorates for deep GCNs, as message passing over the observed noisy graph topology cannot adequately propagate intra-class information and over-mix the features of nodes from different communities (classes). Existing topology optimization methods for GCNs cannot sufficiently exploit the underlying ground-truth community structure to distinguish nodes from different communities. In this paper, we propose a novel method, termed EM-GCN, to address this issue by employing the Expectation Maximization (EM) algorithm to simultaneously achieve community-enhanced topology optimization and learn desirable node representations for classification. EM-GCN represents the underlying community structure with a latent adjacency matrix parameterized by an assortative-constrained stochastic block model, and consequently, explicitly enhances intra-class connection and suppresses inter-class interaction in the observed noisy graph. In the inference procedure (E-step), a graph inference model shared across all the node pairs is learned from node embeddings to approximate the posterior distribution of the latent adjacency matrix and optimize the graph topology. In the learning procedure (M-step), node representations are learned using GCNs based on the refined graph topology for the downstream classification task. EM-GCN is a general and flexible method that leverages approximate posterior and arbitrary GCNs for overcoming over-smoothing with topology optimization. Experimental results on synthetic and real-world datasets demonstrate that EM-GCN outperforms existing strategies for tackling over-smoothing and optimizing graph topology in node classification.