Reviewerly/exHarmony huggingface dataset:
Combining the Transformers and CNNs for Renal Parenchymal Tumors Diagnosis

Convolutional neural networks (CNNs) can be used to automatically classify the subtypes of renal parenchymal tumors. However, such approaches may overlook the long-range dependencies of features. In this study, we stacked Transformers and CNNs to efficiently capture both long-range dependencies of features and low-level spatial details. The optimal stack layout, the width, and the depth of the networks were determined according to the characteristics of the renal tumors. In addition, we adopted transfer learning for better performance. The experiments conducted on the T2-weighted magnetic resonance (T2W-MR) images from 199 patients presented an 82.9% overall classification accuracy and 0.96 average AUC. The results demonstrate that combining CNN with Transforms is an effective strategy for renal parenchymal tumors diagnosis.
Current Status Analysis on Early Stage Support of Technology Based Initiatives

Innovation is a critical concept for both today’s national economies and institutions. In this context, innovation is needed to ensure regional and national sustainable growth, to establish competitiveness and to improve the current level of welfare. When we look at the supports and investments made for technology-based entrepreneurs in our country, public and private sector supports, incubation centers and acceleration program activities are seen. In this study, an analysis of the current situation of early stage support of technology based initiatives in our country has been made. In order to collect the necessary information and data for the research, a questionnaire study was directed to a total of 80 technology companies, venture capital investment funds and academician companies operating in Informatics Valley, which is not a university-supported institution in Turkey.
Augmenting a Nature Documentary with a Lifelike Hologram in Virtual Reality

While augmented reality television (ARTV) is being investigated in research labs, the high cost of AR headsets makes it difficult for audiences to benefit from the research. However, the relative affordability of virtual reality (VR) headsets provides ARTV researchers with opportunities to test their prototypes in VR. Additionally, as VR becomes an acceptable medium for watching conventional TV, augmenting such viewing experiences in VR creates new opportunities. We prototype a nature documentary ARTV experience in VR and conduct a remote user study (n = 10) to investigate six points on the visual display design dimension of presenting a lifelike programme-related hologram. We manipulated the starting point and the movement behaviour of the hologram to gain insight into viewer preferences. Our findings highlight the importance of personal preferences and that of the perceived role of a hologram in relation to the underlying TV content; suggesting there may not be a single way to augment a TV programme. Instead, creators may need to provide the audiences with capabilities to customise ARTV content.
Conversational recommendation: A grand AI challenge

Abstract Animated avatars, which look and talk like humans, are iconic visions of the future of AI‐powered systems. Through many sci‐fi movies, we are acquainted with the idea of speaking to such virtual personalities as if they were humans. Today, we talk more and more to machines like Apple's Siri, for example, to ask them for the weather forecast. However, when asked for recommendations, for example, for a restaurant to go to, the limitations of such devices quickly become obvious. They do not engage in a conversation to find out what we might prefer, they often do not provide explanations for what they recommend, and they may have difficulties remembering what was said 1 min earlier. Conversational recommender systems (CRS) promise to address these limitations. In this paper, we review existing approaches to building such systems, which developments we observe today, which challenges are still open and why the development of conversational recommenders represents one of the next grand challenges of AI.
Isotope spatiotemporal analysis and prospecting indication based on GIS in Tibet

Tibet has received much attention as a pivotal area to explore the genesis of ore deposits. So far, a large number of researchers have conducted a lot of discussions on the geological conditions, genesis, distribution and scale of ore deposits in Tibet, and accumulated a plenty of isotopic test data. Isotopic data can reveal the geological changes of an area and analyze the metallogenic regularity. In this paper, combined with GIS (Geographic Information System) spatiotemporal analysis technology, Hf and Nd isotopic data mapping analysis has been carried out in Tibet. The mapping shows high zircons εHf(t) value and high whole rock εNd(t) value and low TDMc value on both sides of the Eastern Yarlung Zangbo Suture Zone and the Western Bangong-Nujiang Suture Zone. It indicates that the terranes in these two areas are relatively young and have typical characteristics of Cu and Au mineralization, which are favorable areas for the prospecting of Cu and Au. Combined with the geological fault characteristics of Tibet Plateau, the influence of terrane boundary on the deposit and the relevant characteristics of geochemical elements, three favorable targets for Cu and Au prospecting in Tibet Plateau are delineated by GIS analysis. The results show that the characteristics of isotopic data in Tibet Plateau are quite different in disparate terranes, and the isotopic analysis can quickly delineate the strategic prospecting targets. Therefore, using GIS technology for isotope analysis and metallogenic prediction provides important guiding significance for scientific prospecting.
Operation Optimization of Supply Chain Financial System Based on System Dynamics

Supply chain finance solves the problem of financing difficulties for small- and medium-sized enterprises, reduces the risk of financial services of financial institutions, and brings new profit growth points to logistics enterprises. Therefore, supply chain financing has achieved rapid growth in a short period of time in China. This paper aims to study how to analyze and study the collaborative development of supply chain finance based on system dynamics. This paper puts forward the problem of system operation optimization, which is based on system dynamics, then elaborates on the concepts of SD and SCF, and designs and analyzes a case of a supply chain financial system based on system dynamics. The experimental results show that increasing the "bank input" from 0.1 to 2 can bring considerable economic benefits in the long run. As far as the simulation results are concerned, the promotion effect on the supply chain is the most significant. The output value of the supply chain can be increased from 2,963.04 billion yuan to 4,211.73 billion yuan, an increase of 42.14%. The simulation results showed that the financial industry has a significant positive effect on the factor of supply chain support, which provides evidence support for further guiding practice.
HEVC Video Quality Enhancement Using Deep Learning with Super Interpolation and Laplacian Filter

Enhancing video quality is a vital area of research that has piqued the interest of academia and industry alike. The High-Efficiency Video Coding (HEVC) compressed videos suffer from significant quality loss, especially at low bit rates. As a result, the decoder must improve the image quality of HEVC videos. Deep Learning with Super Interpolation based Super Resolution (DLSI-SR) and Laplacian Filter (LF) method is proposed to eliminate artifacts. A Multi-Layered Deep Convolutional Neural Network (ML-DCNN) with LF has been introduced to reduce the artifacts quickly in compressed video and increase the subjective video quality. The quality of Low-Resolution (LR) frames can be effectively enhanced using Super Interpolation and Laplacian filter. The Edge orientation analysis is performed on various external training video frames during the training stage. The LR video frame is up-sampled and interpolated using the Super Interpolation algorithm during the upscaling phase. The interpolated frame is then submitted to edge detection using a Canny Edge detection algorithm for frame smoothing. Using local Laplacian filter, the reconstructed high quality video frame is sharpened with an edge preservation approach. The simulation results of the proposed DLSI-SR method shows higher performance in terms of PSNR and SSIM with less processing time in comparison with the existing approaches. The experimental results show that the proposed approach achieves a maximum PSNR of 42.5 dB (enhanced by 2.56 dB) and produced an SSIM improvement of 0.948 (enhanced by +0.022) as compared to the existing approaches.
Machine Learning Classifiers for Detecting Credit Card Fraudulent Transactions

Credit card usage has increased significantly as a result of the fast development of e-commerce and the Internet. As a consequence of enhanced credit card usage, credit card theft has risen substantially in recent years. Fraud in the financial sector is expected to have far-reaching effects in the near future. As a response, numerous scholars are concerned with financial fraud detection and prevention. In order to prevent bothering innocent consumers while detecting fraud, accuracy has become critical. We used hyperparameter optimization to see if created models utilizing different machine learning approaches are significantly the same or different, and if resampling strategies improve the suggested models' performance. The hyperparameter is optimized using GridSearchCV techniques. To test the hypotheses of data that has been divided into training and test data, the GridSearchCV and random search methods are used. The maximum accuracy 72.1% was achieved by decision tree classifier on the imbalanced German credit card dataset. The maximum accuracy of 98.6% is achieved by LDA on imbalanced European credit card dataset. Additionally, logistic regression and naïve Bayes were also tested and SMOTE was applied.
Smart systems of open science in teachers’ education

Abstract The project announced by the authors is focused on the smart systems of open science, the issues of elaboration of methodological, methodic, and organizational support for the use of these emerging ICT in teaching and research, increasing the level of ICT competence of educators, including teachers. Given the significant pedagogical potential and novelty of existing approaches to the use of smart learning systems meaning first of all the cloud-based adaptive open science systems, their design and use in educational institutions, these issues still need theoretical and experimental research, refinement of approaches, models, methods and techniques, possible ways of implementation. In the process of studying leading experience, the following advantages of using the cloud-based services for teachers’ training were identified: resource savings, access mobility, elasticity, and others. The cloud-based methodological system for educational personnel training at different levels was proposed and considered: for the training of students for the Master level for educational sciences; for the in-service teachers’ training. The methods and tools of smart systems design and implementation were considered and evaluated. The recommendations and suggestions for the advisable ways for smart systems of open science introduction into the learning process were provided.
Localized compensator phase unwrapping algorithm based on flux conservable solver

Phase unwrapping is a critical and challenging step in any phase measurement system. Localized compensator phase unwrapping method (LC) has a good accuracy to regularize singularity for noisy wrapped phase data. Since the LC method can compensate the singularity in limited area to local region around singular points (SPs). However, the LC method has two big problems; firstly it requires large amounts of computational time to produce its unwrapped results. Secondly, this method has limitation of memory shortage if the limited area size including SPs is large, hence the LC method cannot produce unwrapped results. Most of the computational time in LC method is exhausted in the process of fixing numerical error of the compensators that are computed from accumulating the solution of Poisson’s equation using boundary element method and fix errors using singular valued decomposition method. In order to solve the LC method’s problems and improve its performance, we propose a new phase unwrapping algorithm based on computing the compensators of singularity cluster regions directly. In the proposed algorithm, the process of fixing the errors is unnecessary, since it considers the compensators as flux of potential vectors and computes them from solving Poisson’s equation directly. The applicability of the proposed method is tested and evaluated on both simulated and experimental phase data. The unwrapped results show that the proposed algorithm can reduce the computational time; meanwhile, it maintains the same accuracy of unwrapped results as the original algorithm with the localized compensator does.
Deep Learning Augmented Realistic Avatars for Social VR Human Representation

Virtual reality (VR) has created a new and rich medium for people to meet each other digitally. In VR, people can choose from a broad range of representations. In several cases, it is important to provide users with avatars that are a lifelike representation of themselves, to increase the user experience and effectiveness of communication. In this work, we propose a pipeline for generating a realistic and expressive avatar from a single reference image. The pipeline consists of a blendshape-based avatar combined with two deep learning improvements. The first improvement module runs offline and improves the texture map of the base avatar. The second module runs inference in real-time at the rendering stage and performs a style transfer to the avatar’s eyes. The deep learning modules effectively improve the visual representation of the avatar and show how AI techniques can be integrated with traditional animation methods to generate realistic human avatars for social VR.
Exploring Effect of Level of Storytelling Richness on Science Learning in Interactive and Immersive Virtual Reality

Immersive and interactive storytelling in virtual reality (VR) is an emerging creative practice that has been thriving in recent years. Educational applications using immersive VR storytelling to explain complex science concepts have very promising pedagogical benefits because on the one hand, storytelling breaks down the complexity of science concepts by bridging them to people's everyday experiences and familiar cognitive models, and on the other hand, the learning process is further reinforced through rich interactivity afforded by the VR experiences. However, it is unclear how different amounts of storytelling in an interactive VR storytelling experience may affect learning outcomes due to a paucity of literature on educational VR storytelling research. This preliminary study aims to add to the literature through an exploration of variations in the designs of essential storytelling elements in educational VR storytelling experiences and their impact on the learning of complex immunology concepts.
Intertextuality between French Literature Creation and Literature Translation Based on Feature Extraction and Gram Matrix

In the 1980s, there was a cultural turn in translation studies. Since then, translation studies are no longer confined to the study of specific texts, but examine the texts in the context of culture and history, and study the production process of texts and the relationship between texts and related factors such as culture and history. When literary works are transplanted from one language to another, it is like moving plants and animals from one place to another. They must “adapt” and grow like individuals or nations, and they can only survive if they adapt to the new environment and change. Intertextuality is one of the important terms in contemporary literary criticism. Intertextuality is one of the seven textual features of a text (formal adaptation, semantic adaptation, intentionality, receptivity, informality, situational, and intertextuality). Gram incidence matrix is designed to count and merge feature words using feature extraction algorithm for text processing and feature extraction, so that feature words of different lengths can be extracted using fixed-length algorithm. A poststructuralist, Roland Barthes, deconstructs the traditional concept of text and extends it to the social and cultural context. The so-called intertextuality of literary creation and literary translation also incorporates cultural connotation and knowledge structure into the relationship between texts. Literary system is a system, social culture is a larger system, and literary system is a constituent factor of the social cultural system, according to the theory of multiple systems; literary creation and literary translation are factors in the literary system. The main focus of intertextuality theory is text comprehension. Bloom’s intertextuality theory, on the other hand, expounds on text creation. On the basis of a feature extraction algorithm, this paper investigates the types and techniques of intertextuality between creative text and translated text using a combination of objective description and theoretical analysis.
Optimal Sensor Placement Using Learning Models—A Mediterranean Case Study

In this paper, we discuss different approaches to optimal sensor placement and propose that an optimal sensor location can be selected using unsupervised learning methods such as self-organising maps, neural gas or the K-means algorithm. We show how each of the algorithms can be used for this purpose and that additional constraints such as distance from shore, which is presumed to be related to deployment and maintenance costs, can be considered. The study uses wind data over the Mediterranean Sea and uses the reconstruction error to evaluate sensor location selection. The reconstruction error shows that results deteriorate when additional constraints are added to the equation. However, it is also shown that a small fraction of the data is sufficient to reconstruct wind data over a larger geographic area with an error comparable to that of a meteorological model. The results are confirmed by several experiments and are consistent with the results of previous studies.
An Efficient Accelerator with Winograd for Novel Convolutional Neural Networks

In recent years, the current trend of Convolutional Neural Networks (CNNs) is toward lower computational cost to achieve lightweight. In lightweight convolutional neural networks, the depthwise separable convolution (DSC) is becoming the mainstream method. But in DSC, the pointwise convolution (PWC) with $1\times 1$ filters still has abundant parameters and computation. In this paper, an more efficient convolution algorithm is proposed to replace PWC, named kernel shared group convolution (KSGC). KSGC is used to combine channel information, which can be seen as the same convolution kernel sliding on the channel. In addition, Winograd algorithm is used to mitigate the number of multiplications required by KSGC in this paper. A CNN accelerator using a novel processing element (PE) performs 1-D Winograd in KSGC was implemented on a Ultra96-V2 field-programmable gate array (FPGA).At 200MHz clock frequency, the accelerator achieved computational performance of 52. 7GOPS and performance-power ratio of 10.42GOPS/W.
Early SoCs Information Flow Policies Validation using SystemC-based Virtual Prototypes at the ESL

Virtual Prototypes (VPs) at the Electronic System Level (ESL) are being increasingly adopted by the semiconductor industry and play an important role in modernizing the System-on-Chips (SoCs) design flow to raise design productivity and reduce time-to-market constraints. Due to their early availability and significantly faster simulation speed in comparison to Register Transfer Level (RTL) designs, VPs are used as reference models for lower levels of abstraction. Leveraging VPs and extending their use-cases for early security validation are shown as a promising direction. As the cost of fixing any security flaws increases with the stage of development, VP-based security validation can significantly avoid costly iterations. In this paper, we present a novel VP-based dynamic information flow analysis approach at the ESL, consisting of three main phases which are run-time behavior extraction (in terms of transactions), transactions transformation, and security validation. The proposed approach empowers designers to validate the information flow policies of a given VP-based SoC against the most occurring security threat models which are information leakage (confidentiality) and unauthorized access to data in a memory (integrity). Experimental results including an extensive set of standard benchmarks and two real-world VP-based SoCs demonstrate the scalability and applicability of the proposed approach.
Prediction of palm oil production in Riau Province using the single exponential smoothing method

Abstract Riau Province is one of the provinces in Sumatra with the most oil palm plantations. Riau Province was once recognized as Indonesia’s greatest oil-producing province, but with the fall in output owing to decreasing oil reserves, Riau has become a greater palm oil producer. Not unexpectedly, palm oil has surpassed petroleum in terms of foreign exchange in Indonesia since 2010. There are numerous stages in this research, namely problem analysis, data collection, data preprocessing, single exponential smoothing prediction, data modeling, and calculation precision. Based on the findings of the testing, the Single Exponential Smoothing technique with alpha 0.9 may be used to forecast the requirement for prior time series data in the future, particularly in the case of oil palm output in Riau Province. Oil palm production is expected to grow somewhat over the next five years, according to the forecast.
Privacy protection generalization with adversarial fusion

Several biometric privacy-enhancing techniques have been appraised to protect face image privacy. However, a face privacy protection algorithm is usually designed for a specific face recognition algorithm. When the structure or threshold of the face recognition algorithm is fine-tuned, the protection algorithm may be invalid. It will cause the network bloated and make the image distortion target multiple FRAs through the existing technology simultaneously. To address this problem, a fusion technology is developed to cope with the changeable face recognition algorithms via an image perturbation method. The image perturbation is performed by using a GAN-improved algorithm including generator, nozzles and validator, referred to as the Adversarial Fusion algorithm. A nozzle structure is proposed to replace the discriminator. Paralleling multiple face recognition algorithms on the nozzle can improve the compatibility of the generated image. Next, a validator is added to the training network, which takes part in the inverse back coupling of the generator. This component can make the generated graphics have no impact on human vision. Furthermore, the group hunting theory is quoted to make the network stable and up to 4.8 times faster than other models in training. The experimental results show that the Adversarial Fusion algorithm can not only change the image feature distribution by over 42% but also deal with at least 5 commercial face recognition algorithms at the same time.