Reviewerly/exHarmony huggingface dataset:
Learning to Dynamically Select Cost Optimal Schedulers in Cloud Computing Environments

The operational cost of a cloud computing platform is one of the most significant Quality of Service (QoS) criteria for schedulers, crucial to keep up with the growing computational demands. Several data-driven deep neural network (DNN)-based schedulers have been proposed in recent years that outperform alternative approaches by providing scalable and effective resource management for dynamic workloads. However, state-of-the-art schedulers rely on advanced DNNs with high computational requirements, implying high scheduling costs. In non-stationary contexts, the most sophisticated schedulers may not always be required, and it may be sufficient to rely on low-cost schedulers to temporarily save operational costs. In this work, we propose MetaNet, a surrogate model that predicts the operational costs and scheduling overheads of a large number of DNNbased schedulers and chooses one on-the-fly to jointly optimize job scheduling and execution costs. This facilitates improvements in execution costs, energy usage and service level agreement violations of up to 11%, 43% and 13% compared to the state-of-the-art methods.
Efficient Federated Learning with Self-Regulating Clients

Motivation. Since its inception [6], Federated Learning (FL) has been enjoying a strong interest from the privacypreserving AI research community. FL also has been commercially implemented in popular applications such as Google's Gboard and Apple Siri. In FL, Machine Learning (ML) models are trained cooperatively by many clients (e.g., mobile phones) without explicitly sharing their identity and private data. The training is done through iterative communication rounds between a central parameter/model server and clients. In each communication round, the global model is supplied to a subset of clients who use their private data to do local training on their respective devices. The updated models from each participating client are anonymously sent back to the central server and aggregated to get the global model for the next communication round.
Technopark formation with reagrd to wind power industry

The article describes the formation of technology parks with regard to the wind power industry in the Rostov region. The main aspects of urban planning and functional planning are formulated for technoparks, the production component of wind power systems with consideration of existing settlements, logistics ties, production needs, and economic efficiency. Purpose: To formulate the main aspects of promotion and organization of technology parks focused on the development of the wind power systems in the Rostov region. Research findings: The analysis is given to the scientific and production components of the wind power industry, the main functional and planning aspects of the IT sphere, and the production component focused on the alternative wind power. In this regard, the possibility of creating scientific clusters contributing to the quality of life and attracting leading specialists to the region is identified. Novelty: Consideration and further development of the functional and planning structure of technoparks focused on the promotion and development of the regional wind power industry.
Practical Efficient Microservice Autoscaling

Motivation. In recent years, the adoption of microservices in production systems has been steadily growing. With their loosely-coupled and lightweight components, microservices are easier to manage than traditional monolithic applications. However, microservices introduce new challenges towards efficient resource management because their high number of individually manageable components creates a large configuration space. Moreover, microservices have complex inter-dependencies, making identifying "good" configurations harder. Even the same amount of resources can result in different response latencies based on how the resource is distributed among different microservices. Meanwhile, existing cloud management approaches are designed for monolithic deployments and cannot capture the complex interactions between microservices.
Overcoming the Long Horizon Barrier for Sample-Efficient Reinforcement Learning with Latent Low-Rank Structure

Reinforcement learning (RL) methods have been increasingly popular in sequential decision making tasks due to its empirical success. However, large state and action spaces in real-world problems modeled as a Markov decision processes (MDPs) limit the use of RL algorithms. Given a finite-horizon MDP with state space S, action space A, and horizon H, one needs Ω |S|A|H3/ε2 samples given a generative model to learn an optimal policy [3], which can be impractical when S and A are large. The above tabular RL framework does not capture the fact that many realworld systems in fact have additional structure that if exploited should improve computational and statistical efficiency. Moreover, [1] empirically verifies that optimal and near-optimal action-value functions (both viewed as |S|-by- |A| matrices) of classical stochastic control tasks have low rank. Thus, the critical question is what are the minimal low rank structural assumptions that allow for computationally and statistically efficient learning.
Symbol-level iterative information set decoding of RS codes

This paper presents the implementation of a low-complex iterative symbol-level decoding scheme for Reed-Solomon codes. Most soft-decision iterative decoders for Reed-Solomon codes work on a bit level due to the efficient passing of soft information due to the sparsity of the binary parity check matrix. These decoders yield a good error correction performance, but this comes at the cost of an increased computational complexity resulting from working at a bit-level. This study aims to lower the computational complexity of iterative decoding of Reed-Solomon codes by introducing a high-performance soft-decision iterative Reed-Solomon decoder that works on a symbol-level, in contrast to the bit-level implementation used in most iterative Reed-Solomon decoders. The proposed algorithm utilises soft information to decode while applying information set decoding techniques to an extended parity check matrix. The use of the extended parity check matrix provides additional parity check equations which assist the algorithm in determining the correct information set of symbols used in the decoding process. For a given (n,k) Reed-Solomon code, the algorithm converges to a valid codeword by correctly decoding a specified information set of k symbols from the received vector. Simulations run show the proposed algorithm performs favourably when compared to other symbol-level iterative decoders while working at a relatively lower complexity.
Neural noiseprint transfer: a generic noiseprint-based counter forensics framework

A noiseprint is a camera-related artifact that can be extracted from an image to serve as a powerful tool for several forensic tasks. The noiseprint is built with a deep learning data-driven approach that is trained to produce unique noise residuals with clear traces of camera-related artifacts. This data-driven approach results in a complex relationship that governs the noiseprint with the input image, making it challenging to attack. This article proposes a novel neural noiseprint transfer framework for noiseprint-based counter forensics. Given an authentic image and a forged image, the proposed framework synthesizes a newly generated image that is visually imperceptible to the forged image, but its noiseprint is very close to the noiseprint of the authentic one, to make it appear as if it is authentic and thus renders the noiseprint-based forensics ineffective. Based on deep content and noiseprint representations of the forged and authentic images, we implement the proposed framework in two different approaches. The first is an optimization-based approach that synthesizes the generated image by minimizing the difference between its content representation with the content representation of the forged image while, at the same time, minimizing the noiseprint representation difference from the authentic one. The second approach is a noiseprint injection-based approach, which first trains a novel neural noiseprint-injector network that can inject the noiseprint of an image into another one. Then, the trained noiseprint-injector is used to inject the noiseprint from the authentic image into the forged one to produce the generated image. The proposed approaches are generic and do not require training for specific images or camera models. Both approaches are evaluated on several datasets against two common forensic tasks: the forgery localization and camera source identification tasks. In the two tasks, the proposed approaches are able to significantly reduce several forensic accuracy scores compared with two noiseprint-based forensics methods while at the same time producing high-fidelity images. On the DSO-1 dataset, the reduction in the forensic accuracy scores has an average of 75%, while the produced images have an average PSNR of 31.5 dB and SSIM of 0.9. The source code of the proposed approaches is available on GitHub (https://github.com/ahmed-elliethy/nnt).
Feature Equilibrium: An Adversarial Training Method to Improve Representation Learning

Abstract Over-fitting is a significant threat to the integrity and reliability of deep neural networks with generous parameters. One problem is that the model learned more specific features than general features in the training process. To solve the problem, we propose an adversarial training method to assist the model in strengthening general representation learning. In this method, we make a classification model as a generator G and introduce an unsupervised discriminator D to distinguish the hidden feature of the classification model from real images to limit their spatial distance. Notably, the D will fall into the trap of a perfect discriminator resulting in the gradient of confrontation loss of 0 after overtraining. To avoid this situation, we train the D with a probability $$P_{c}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mi>P</mml:mi> <mml:mi>c</mml:mi> </mml:msub> </mml:math> . Our proposed method is easy to incorporate into existing frameworks. It has been evaluated under various network architectures over different fields of datasets. Experiments show that this method, under low computational cost, outperforms the benchmark by 1.5–2 points on different datasets. For semantic segmentation on VOC, our proposed method achieves 2.2 points higher mAP.
Modelling Proper and Improper Sitting Posture of Computer Users Using Machine Vision for a Human–Computer Intelligent Interactive System during COVID-19

Human posture recognition is one of the most challenging tasks due to the variation in human appearance, changes in the background and illumination, additional noise in the frame, and diverse characteristics and amount of data generated. Aside from these, generating a high configuration for recognition of human body parts, occlusion, nearly identical parts of the body, variations of colors due to clothing, and other various factors make this task one of the hardest in computer vision. Therefore, these studies require high-computing devices and machines that could handle the computational load of this task. This study used a small-scale convolutional neural network and a smartphone built-in camera to recognize proper and improper sitting posture in a work-from-home setup. Aside from the recognition of body points, this study also utilized points’ distances and angles to help in recognition. Overall, the study was able to develop two objective datasets capturing the left and right side of the participants with the supervision and guidance of licensed physical therapists. The study shows accuracies of 85.18% and 92.07%, and kappas of 0.691 and 0.838, respectively. The system was developed, implemented, and tested in a work-from-home environment.
Using High-Level Representation Difference Constraint and Relative Reconstruction Constraint for Defending against Adversarial Attacks

Adversarial examples in which imperceptible perturbations to the input can easily subvert a well-trained model’s prediction pose huge potential security threats to deep neural networks (DNNs). As an effective way to resist adversarial samples, input reconstruction can eliminate the antagonism of adversarial examples in the inference process without involving modifications to the target model’s structure and parameters. However, preprocessing inputs often results in some loss of the protected model’s prediction accuracy. In this paper, we introduce a new input reconstruction method that adopts the high-level representation difference constraint and the relative reconstruction constraint on a dual autoencoder to advance the prediction accuracy of the protected model. The high-level representation difference constraint utilizes the gap between the protected model’s high-level representations, activated by clean images, and their adversarial examples to guide the training of the dual autoencoder. Additionally, the relative reconstruction constraint is imposed on latent representations and their noisy versions to advance the robustness of the dual autoencoder to tiny perturbations. The extensive empirical experiments on two real datasets, CIFAR-10 and ImageNet, show that the presented approach demonstrates exceptional performance in resisting different types of attacks.
Potential field-based dual heuristic programming for path-following and obstacle avoidance of wheeled mobile robots

Abstract Path-following control of wheeled mobile robots has been a crucial research topic in robotic control theory and applications. In path-following control with obstacles, the path-following control and collision avoidance goals might be conflicting, making it challenging to obtain near-optimal solutions for path-following control and obstacle avoidance with low tracking error and input energy consumption. To address this problem, we propose a potential field-based dual heuristic programming (P-DHP) algorithm with an actor–critic (AC) structure for path-following control of mobile robots with obstacle avoidance. In the proposed P-DHP, the path-following control and collision avoidance problems are decoupled into two ones to resolve the control conflict. Firstly, a neural network-based AC is constructed to approximate the near-optimal path-following control policy in a no-obstacle environment. Then, with the trained path-following control policy fixed, a potential field-based control policy structure is constructed by another AC network to generate opposite control forces as the robot moves toward the obstacle, which can guarantee the robot’s control safety and reduce the tracking error and input energy consumption in obstacle avoidance. The simulated and experimental results show that P-DHP can realize near-optimal path-following control with the satisfaction of safety constraints and outperforms state-of-the-art approaches in control performance.
An Offshore Solar Irradiance Calculator (OSIC) Applied to Photovoltaic Tracking Systems

Offshore photovoltaic installations are the future technology in solar energy since they enable the use of the large amount of maritime space, which is especially important when land space is not available. Various research groups are working to create viable installations. However, there are currently no tools available that an offshore system designer can use to quantify the effect of wave response motion on offshore photovoltaic installations. This research presents a new simulation tool termed the Offshore Solar Irradiance Calculator (OSIC) that is able to quantify this effect. Furthermore, a yearly parametric analysis is presented to show the effects of a characteristic wave equation on different offshore tracking systems; namely, horizontal single-axis tracking, vertical single-axis tracking and dual-axis tracking. Finally, another parametric analysis is presented to show the effects of varying wave amplitudes of oscillations on the incident irradiance received by these tracking systems.
Environment-assisted qubit–qutrit entanglement restoration via weak measurement reversal

Abstract Based on the quantum technique of environment-assisted measurement and reversal (EMR), we propose a scheme to restore the entanglement from amplitude damping decoherence. In particular, we examine two typical families of initially entangled qubit–qutrit states and derive the exact analytical expressions of the optimal final restoration, showing explicitly that the EMR does indeed can recovery qubit–qutrit entanglement. This sheds new light on the protection of high-dimensional quantum entanglement in complex quantum environment.
State Manipulation via the All‐Microwave Protocol in the Superconducting Qutrits

The quantum gate is the foundation of quantum computation and simulation in superconducting circuits. In recent years, quantum systems with three energy levels have attracted much attention. Therefore, it is essential to study the protocol of the quantum‐state transfer in qutrits. Herein, the all‐microwave schemes are studied to realize the state transfer and quantum gates. First, the ∣ fg &gt;–∣ ge &gt; transition is designed in a superconducting circuit with a relatively large coupling. We measured the Stark shift in cases where the experimental parameters did not satisfy the dispersive condition. Then, by carefully calibrating the pulse parameters, the regular Rabi oscillation and the state transfer are realized between the second excited state of two qubits via stimulated Raman adiabatic passage.
Modes mismatch induced variation of quantum coherence for two-mode localized Gaussian states in accelerated frame

Quantum coherence is the basic concept of superposition of quantum states and plays an important role in quantum metrology. We show how a pair of uniformly accelerated observers with a local two-mode Gaussian quantum state affects the Gaussian quantum coherence. We find that the quantum coherence decreases with increasing acceleration, which is due to the Unruh effect that destroys the quantum resource. Essentially, the variation of quantum coherence is caused by the modes mismatch between the input and output mode. Through 2000 randomly generated states, we demonstrate that such mismatch is dominated by the acceleration effect and mildly affected by the waveform parameters. Moreover, the squeezing parameter acted as a suppressor of the reduced coherence, but it tended to be invalid in the high squeezing. In addition, the squeezing parameter can act as a suppressor of the reduced coherence, but the effect of the squeezing parameter tends to be ineffective under high squeezing conditions.
Malicious URL Detection using Machine Learning

Currently, the risk of network information insecurity is increasing rapidly in number and level of danger. The methods mostly used by hackers today is to attack end-to-end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. One of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As a results, malicious URL detection is of great interest nowadays. There have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behaviors and attributes.
“I’m reminding us of where we came from”: an interview with Nick Sousanis

ABSTRACTABSTRACTIn Fall 2022, we sat down with Nick Sousanis, Eisner-Winning comics author, and the creator of Unflattening to talk about his forthcoming work, Nostos. He shared with us his research process and the formalistic challenges he has given himself to explore the capabilities of comics to document and display. We talked about making comics as a way of thinking and processing ideas including the birth of the universe, conception, and the development of a child. We also hear stories from the classroom as Nick Sousanis shares unique examples of comics from non-drawers and finally, we consider the future of comics for blind and low- vision readersKEYWORDS: Creativitycomics-makingchild developmentOdysseyblind and low vision Disclosure statementNo potential conflict of interest was reported by the author(s).
Steganalysis of Chat Based Steganography

Steganalysis is the practice of identifying potential secret communication and taking appropriate action, such as deciphering to uncover the hidden contents or destroying the object containing the hidden information if it cannot be uncovered. At times, it’s very necessary to perform Steganalysis due to the fact that steganography is often misused by those with bad intentions, making it a platform for criminal communication. This paper presents a methodology for the detection of timing steganography. The method is based on user behavior during chat, such as the time taken to read, edit, and send text, etc. The method was tested using simulation-based chat software, and it can detect intended timing samples correctly.
NCC: Neural concept compression for multilingual document recommendation

In this work, we propose a novel method for generating inter-lingual document representations using neural network concept compression. The presented approach is intended to improve the quality of content-based multilingual document recommendation and information retrieval systems by creating a language-independent representation. The main idea is to use mappings to align monolingual representation spaces, using concept compression, to create inter-lingual representations. The proposed approach outperforms traditional cross-lingual retrieval and recommendations methods in experiments conducted on JRC-Acquis and EU bookshop multilingual corpora. Our dataset and code are publicly available at https://github.com/Tsegaye-misikir/NCC.
Super-resolution model-based iterative reconstruction for lens-coupled micro-CT imaging

Abstract Lens-coupled high-resolution micro-computed tomography (micro-CT) uses a visible light magnification system behind the x-ray path to achieve higher resolution imaging than conventional micro-CT. However, the spatial resolution is theoretically limited by optical diffraction and mechanical control precision. As a result, the current system resolution is still insufficient for some applications, such as the imaging of biological materials whose structures are on the nanometer scale. To overcome this limitation, a super-resolution algorithm can be employed to improve the image resolution beyond the theoretical upper bound of the ideal spatial resolution of the system. In this work, a super-resolution model-based iterative reconstruction (SR-MBIR) algorithm is proposed based on a lens-coupled high-resolution micro-CT system and a high-precision nano-stage attached to the rotation stage of the system. The algorithm employs a scanning program that dithers the object via the nano-stage to obtain multiple sets of projection images with sub-pixel information. The blur and noise statistical models are introduced into the physical model for iterative reconstruction, allowing for super-resolution, deblurring, and noise suppression. The results of simulation data and actual data show that the SR-MBIR algorithm has a prominent effect in improving image resolution. The reconstructed images have sharper edges, better details, higher signal-to-noise ratio, and can effectively suppress the systematic blur and noise in the imaging process, thus achieving superior interior reconstruction quality.