Reviewerly/exHarmony huggingface dataset:
Graphics of Ukrainian Children’s Publications 2000–2010: Based on the Work of LNAM Graduates

The article analyzes the experience of some illustrators, graduates of the Lviv National Academy of Arts, whose creative program has developed since the beginning of the XXI century. There is a general approach to solving the system “author–text–vision”, stylistics, metaphorical and plastic imagery, interpretation of elements of ethnic tradition and modern design technologies. The object of research is the graphics of children’s publications of Ukrainian publishers that specialize in publishing books for children. For the first time, the creative work of these artists is analyzed: illustration, decoration, created by means of computer graphics. Children’s books of the Lviv edition. The subject of the research is figurative and expressive means of illustrators of Ukrainian children’s publications of 2000–2010. The methodological basis is the historical and cultural consideration. Scientific methods will be used in the research: descriptive, historical-genetic analysis, comparative and semantic analysis, and others. (1) Descriptive method, thanks to which we classify the artistic and technical execution of children’s books. (2) Comparative analysis will allow us to compare historical and literary sources and research. (3) Semantic analysis, according to which we will trace and compare “textual facts” on the basis of descriptive, historical, comparative methods.
Experimental robustness of the fractional topological phase to dephasing noise

We observe experimentally the fractional topological phase (FTP) robustness to the dephasing noise in two-qudit photonic systems. We use a source of photon pairs generated by spontaneous parametric down-conversion, hyperentangled in the polarization and photon path degrees of freedom. The dephasing noise is described theoretically via Kraus maps and experimentally implemented via the spatial light modulator. The FTP is a great candidate to explore in the implementation of quantum processing and communication due to its robustness to dephasing noise.
Improving Co-speech gesture rule-map generation via wild pose matching with gesture units.

In this poster, we present a method to generate co-speech text-to-gesture mapping for 3D digital humans. We obtained text and 2D pose data from public monologue videos. Gesture units were obtained from motion capture sequences. The method works by matching 2D poses to 3D gesture units. We trained a model via contrastive learning to improve the matching of noisy pose sequences with gesture units. To ensure diverse gesture sequences at runtime, gesture units were clustered using K-Mean clustering. We incorporated 2035 gestures and 210k rules. Our method is highly adaptable and easy to control and use. Demo Video : https://youtu.be/QBtGdGE1Wgk
The Investigation of Foldable Display Form Factor Notebook System &amp; the Design Challenges

Intel's Client Computing Group's (CCG) project Athena is generating a new wave of exciting Evo brand notebook systems. Project Athena has enabled foldable display systems as a new device category. To influence the industry towards this new device category and technology readiness proof point, CCG has developed a foldable display notebook system called Horseshoe Bend. Horseshoe Bend uses a 17.3-inch foldable OLED display that's being enabled by Intel. This paper discusses the Horseshoe Bend system design challenges, innovations required, and solutions implemented. In general, foldable systems need to support usages of multiple form factors into one physical system- 17.3-inch tabletop All-in-one system, and 12.5-inch notebook when folded in laptop posture. This poses multiple challenges-support orthogonal requirements and resolve foldable display system integration issues. This paper details how this system design solved issues of display displacement absorption to integrate into system chassis, higher performance expectation, orthogonal requirements of input/output (I/O) support, new audio and imaging usages, innovations in printed circuit board (PCB) design, among others.
Improving smart contract search by semantic and structural clustering for source codes

The search for smart contract source codes has drawn research attention to fulfill developers’ and researchers’ needs. Yet, the existing studies are not mature enough to address smart contracts’ technical properties and functionalities. This paper proposes a system to improve the naive search for smart contract codes; for example, Etherscan has one keyword search feature without regard to the contract structure. We consider clustering smart contracts based on developers’ preferences, which increases the probability that the resulting source codes match developers’ needs. Our experimental results show a significant improvement in the complexity of the retrieved source codes of smart contracts compared with the baseline scenario using blockchain search engines (e.g., Etherscan). Our solution reduces the number of retrieved smart contract codes the developer has to check if the codes match her/his needs by 94%, 88%, 82%, or 98%, depending on the user’s search preferences.
Semantic Coherence Dataset: Speech transcripts

The Semantic Coherence Dataset has been designed to experiment with semantic coherence metrics. More specifically, the dataset has been built to the ends of testing whether probabilistic measures, such as perplexity, provide stable scores to analyze spoken language. Perplexity, which was originally conceived as an information-theoretic measure to assess the probabilistic inference properties of language models, has recently been proven to be an appropriate tool to categorize speech transcripts based on semantic coherence accounts. More specifically, perplexity has been successfully employed to discriminate subjects suffering from Alzheimer Disease and healthy controls. Collected data include speech transcripts, intended to investigate semantic coherence at different levels: data are thus arranged into two classes, to investigate intra-subject semantic coherence, and inter-subject semantic coherence. In the former case transcripts from a single speaker can be employed to train and test language models and to explore whether the perplexity metric provides stable scores in assessing talks from that speaker, while allowing to distinguish between two different forms of speech, political rallies and interviews. In the latter case, models can be trained by employing transcripts from a given speaker, and then used to measure how stable the perplexity metric is when computed using the model from that user and transcripts from different users. Transcripts were extracted from talks lasting almost 13 hours (overall 12:45:17 and 120,326 tokens) for the former class; and almost 30 hours (29:47:34 and 252,270 tokens) for the latter one. Data herein can be reused to perform analyses on measures built on top of language models, and more in general on measures that are aimed at exploring the linguistic features of text documents.
Development and validation of an AI-Driven model for the La Rance tidal barrage: A generalisable case study

In this work, an AI-Driven (autonomous) model representation of the La Rance tidal barrage was developed using novel parametrisation and Deep Reinforcement Learning (DRL) techniques. Our model results were validated with experimental measurements, yielding the first Tidal Range Structure (TRS) model validated against a constructed tidal barrage and made available to academics. In order to proper model La Rance, parametrisation methodologies were developed for simulating (i) turbines (in pumping and power generation modes), (ii) transition ramp functions (for opening and closing hydraulic structures) and (iii) equivalent lagoon wetted area. Furthermore, an updated DRL method was implemented for optimising the operation of the hydraulic structures that compose La Rance. The achieved objective of this work was to verify the capabilities of an AI-Driven TRS model to appropriately predict (i) turbine power and (ii) lagoon water level variations. In addition, the observed operational strategy and yearly energy output of our AI-Driven model appeared to be comparable with those reported for the La Rance tidal barrage. The outcomes of this work (developed methodologies and DRL implementations) are generalisable and can be applied to other TRS projects. Furthermore, this work provided insights which allow for more realistic simulation of TRS operation, enabled through our AI-Driven model.
Framework For Data Development in Mordern Situation Using Machine Learning Technology

This research is mainly focused on to talk about data preparation, what better way to start than from observation. Everyone is familiar with the adage that a data scientist should spend 80% of his or her time preparing the data and just 20% actually working with it, particularly when it comes to visualization. This essay will concentrate on data preparation, including the most common issues, solutions, and developments. Data must be put into the proper form before analysis can be done on it. Data manipulation and organization are steps in the preparation of data for analysis. Iteratively transforming unstructured, chaotic raw data into a more organized, practical form that is ready for further analysis is known as data preparation. Data profiling, cleaning, integration, and transformation are just a few of the primary activities (or tasks) that make up the entire preparation process.
A New hyper chaotic algorithm for energy video communication security

The usage of the cameras in facilities in the energy sector is becoming more and more common. In addition, with the widespread use of SCADA and the increase in automation of camera-based applications, ensuring security in video data communication has become more and more important. In this study, methods that have been successful in providing image security in previous studies in Ref.[1] have been improved to ensure the security of video data communication. These methods use Kurt-Modified Chua’s Circuit (KMCC) as random number generator. Proposed algorithms are efficient for energy sector secure video communication because of using hyperchaotic random number generator and also bit level scrambling, diffusion encryption to each frame of video.
Cloud Storage Connected Security Safe

Abstract: Document management system is an essential approach that should be managed well so that it can ensure an effective and faster overall working process. Hardcopy documents have been one of the items that many people and most organizations face difficulty in managing. So, we need to manage them in a safe and secure manner due to the high dependency on them for our day-to-day activity. How to effectively manage softcopy of documents? This is an important issue faced by everyone nowadays. So, in order to improving management efficiency and protecting documents, we proposed a new framework to improve these document management procedures everywhere
LongTail-Bench: A Benchmark Suite for Domain-Specific Operators in Deep Learning

Deep neural net works have brought significant innovations in many domains, such as computer vision, natural language processing, speech recognition, and intelligent decision. To this end, many new operators have also been developed to attain better accuracy in the specific domain, such as the operators about anchors in object detection and operators about agent-environment interaction in reinforcement learning. In this paper, we identify that the new domain-specific operators, which have no corresponding implementation in the device compute library (such as cuDNN, and PyTorch ATen) and have to resort to the Python interpreter, will introduce large amounts of challenges in deep learning training and deployment. We name these operators long-tail operators, inspired by the meaning of the long-tail phenomenon in business and statistics.As such, researchers have developed kinds of deep learning compilers, such as XLA, TorchScript, $\mathcal{J}{A X}$, and TVM, to solve the challenges incurred by long-tail operators. Since there are a lot of complex syntax features in the long-tail operators, providing a well-designed benchmark suite to assess and profile the deep learning compilers is of much importance. Unfortunately, there have been no representative benchmark suites that can take a solid understanding and analysis of long-tail operators. In this paper, we propose LongTail-Bench <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> , a benchmark suite for domain-specific operators in deep learning. To help researchers study deep learning compilers, LongTail-Bench collects more than 100 representative operators. We first perform a comprehensive analysis and show that LongTail-Bench can cover a wide range of syntax features. Then, we conduct a throughout evaluation of the mainstream deep learning compilers from multiple aspects, including training performance, deploy performance, comparison with handwritten code, and performance on the emerging deep learning accelerator. Finally, we take a research example to demonstrate that our benchmark suite can help to support research. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> Corresponding Author
RFCNet: Remote Sensing Image Super-Resolution Using Residual Feature Calibration Network

In the field of single remote sensing image Super-Resolution (SR), deep Convolutional Neural Networks (CNNs) have achieved top performance. To further enhance convolutional module performance in processing remote sensing images, we construct an efficient residual feature calibration block to generate expressive features. After harvesting residual features, we first divide them into two parts along the channel dimension. One part flows to the Self-Calibrated Convolution (SCC) to be further refined, and the other part is rescaled by the proposed Two-Path Channel Attention (TPCA) mechanism. SCC corrects local features according to their expressions under the deep receptive field, so that the features can be refined without increasing the number of calculations. The proposed TPCA uses the means and variances of feature maps to obtain accurate channel attention vectors. Moreover, a region-level nonlocal operation is introduced to capture long-distance spatial contextual information by exploring pixel dependencies at the region level. Extensive experiments demonstrate that the proposed residual feature calibration network is superior to other SR methods in terms of quantitative metrics and visual quality.
Conversion of underwater concrete images to air in detection of hydraulic structures

Abstract Taking underwater concrete images with an optical camera is an important measure for underwater defect detection. However, the underwater low-light environment and light refraction on the surface of different media result in poor image quality. In order to make the collected underwater images effectively reflect the real situation of underwater concrete defects, we propose an image conversion algorithm combining underwater image color enhancement and refraction distortion correction, which can convert underwater images into aerial equivalent images. In this paper, two conversion models of underwater image to air conversion are proposed, one of which models the refractive distortion of the underwater multilayered media through the ray projection method to correct the refractive distortion of underwater images and image field of view (FOV) conversion. The other is that by analyzing the problem of low image-imaging quality and loss of edge information due to the uneven illumination environment underwater, we process the dark channel priority adaptive enhancement algorithm to improve underwater image quality. The experimental results from real scenes show that the imaging quality of underwater images is improved by converting underwater images to air. The pixel error of images converted into the air is ⩽1 pixel, and the FOV error of images is ⩽8.5%. The high-precision underwater image conversion provides strong support for subsequent underwater measurement.
A gamma radiation scene image clarification method based on spatiotemporal combination

Abstract For the problems of complex noise and color cast in the images captured by complementary metal oxide semiconductor image sensors in the gamma radiation environment, this paper proposed a joint temporal correlation denoising and color cast correction approach for the gamma radiation scene image clarification. Concretely, we first adaptively stretch the difference between noise and background to improve the salience of weak noise, making noise detection results more accurate. Secondly, the temporal transient characteristics of gamma radiation noise are utilized for noise detection, and these noisy pixels are repaired by exploiting the temporal correlation characteristics of background pixels. Lastly, the Bayer-Guided auto white balance method is proposed to solve the problem of dysregulated color in the gamma radiation scene image, and the color level remapping approach is applied to eliminate the problem of insufficient dynamic range. Extensive experiments are carried out on the images captured from the Co60 gamma radiation scene. Compared with the source images, our method improves the peak signal to noise ratio by 6.3(dB) and structural similarity by 0.34. Experimental results demonstrate that the proposed method enjoys state-of-the-art performance in improving the clarity of gamma radiation scene images.
Experimentally implementing the step-dependent discrete-time quantum walk on quantum computers

The discrete-time quantum walk (DTQW) with step-dependent (SD) scattering operator is implemented on quantum computer. The probabilities of different states, with their respective fidelities, are calculated. This is done by generalizing the coin with a rotation gate using the quantum gate model. The CNOT gates in the shift operator are replaced with the alternative to CNOT gates R x ( π). They are applied on a quantum device and a quantum simulator (QS). The fidelities varied around 50% and the probability distribution of SD-DTQW for the angle π/4 spread symmetrically, while the step-independent DTQW (SI-DTQW) tended to peak at one side. The symmetric probability distribution of SD-DTQW can help in better control of the walk on QS. In the case of angle π/2, the SI-DTQW spread equally across the states with four peaks, while the SD-DTQW spread with two peaks to one side.
Collecting Multidimensional Numerical Data and Estimating Mean with Personalized Local Differential Privacy

Collecting and analyzing data can generate a wealth of knowledge, but it can also raise privacy concerns. Local differential privacy (LDP) is the latest privacy standard to address this issue and has been implemented on platforms such as Chrome, iOS, and macOS. In the LDP solution, users first perturb their own data on the user side and then upload the perturbed data to the server. This not only protects against background attacks but also against untrusted servers. However, existing multidimensional solutions ignore the personalized privacy needs of users. In this paper, we meet the personalized privacy needs of users while reducing the mean square error of the perturbed data. Specifically, we first designed a personalized privacy budget allocation within a certain range, which meets the personalized privacy needs of users. Then, we optimized the sampling dimension of the existing solution, which resulted in a smaller mean square error of the perturbed data. Finally, we proposed our solution for collecting multidimensional numerical data and estimating the mean. In addition, we conducted experiments on two real datasets. The results demonstrate that the mean square error of our solution is lower than the existing solutions.
Optimizing Energy Efficiency of Node.js Applications with CPU DVFS Awareness

Node.js applications can incorporate CPU Dynamic Voltage and Frequency Scaling (DVFS) to adjust their energy consumption and runtime performance. Thus, we build a CPU frequency scaling policy that promotes “green” and high-performing requests and enables customizations of their execution profile. Our technique requires a profiling step to classify the web requests based on the CPU frequency impact on their energy consumption and runtime performance and on their code syntax/paradigm. We also include the case of concurrent request execution in our model to select an appropriate CPU frequency. We enable priority-based requests to work along with this model for users to customize and formulate a policy based on their goals. Finally, we perform an energy-runtime analysis, which shows that our policy with the proposed configurations is an energy-efficient approach compared to the Linux scaling governors.
Dynamic Multiobjective Optimization Aided by ESN-based Prediction Approach

Dynamic multi-objective problems (DMOPs) have aroused extensive attention in recent years. Prediction-based methods have been proven to be effective. However, most existing methods assume the linear relationships between historical solutions. For real-life systems, ignoring the complex nonlinear relationships between historical environments may result in low prediction accuracy. To solve this problem, the echo state network (ESN) based prediction approach is proposed for DMOPs. First, the reservoir of ESN is used to express the input dynamics of the historical solutions to explore the linear or nonlinear relationships among historical solutions. Then, a fractal interpolation technique (FIT) is introduced to enrich the training data while preserving the original time series features as much as possible. The final experimental results show that the designed algorithm can solve the dynamic multi-objective optimization problems effectively.
Generation of novel Diels–Alder reactions using a generative adversarial network

Deep learning has enormous potential in the chemical and pharmaceutical fields, and generative adversarial networks (GANs) in particular have exhibited remarkable performance in the field of molecular generation as generative models. However, their application in the field of organic chemistry has been limited; thus, in this study, we attempt to utilize a GAN as a generative model for the generation of Diels-Alder reactions. A MaskGAN model was trained with 14 092 Diels-Alder reactions, and 1441 novel Diels-Alder reactions were generated. Analysis of the generated reactions indicated that the model learned several reaction rules in-depth. Thus, the MaskGAN model can be used to generate organic reactions and aid chemists in the exploration of novel reactions.
Human pose estimation: An improvement based on high resolution

Aiming at the large number of parameters in the high-resolution network and the complex operation and easy to cause information loss in the acquisition of low-resolution feature maps, this paper proposes an improved high-resolution human key point detection network DCANet(Dense Coordinate Attention Net). With HRNet as the backbone network, DenseNet was used for lightweight improvement, so as to reduce the operation parameters of the network and optimize the operation speed of the network. At the same time, CA(Coordinate Attention) was added after obtaining low-resolution branches ) module, which extracts channel and location information to overcome the information loss caused by repeated down-sampling operations in the process of acquiring low-resolution feature maps, It can reduce network complexity and ensure accuracy at the same time.