Reviewerly/exHarmony huggingface dataset:
When 360-degree video meets 5G

Live 360° video can provide users with an immersive viewing and interactive experience, and end-to-end live delay is an important metric in quality of experience (QoE). The low latency and high bandwidth nature makes 5G become promising to deliver huge volume of data in 360° video live streaming. However, most of the existing works measured 360° video live delay under non-5G access (e.g., WiFi, LTE or Ethernet). The delay measurement study under 5G access is not comprehensive enough yet. To further validate the feasibility of delivering 360° video live streaming under 5G access, in this paper, we firstly build our 360° video live streaming system based on a series of open-source projects, then measure end-to-end live delay, computation and communication delay of/between each component (i.e., camera, server and client). We find that 5G does behave more superior compared with WiFi and Ethernet access, benefiting from its extremely low downstream delay although its upstream delay is much higher. In addition, we also find that the major delay bottleneck locates in the computation of each stage rather than the communication between them. To further reduce the end-to-end live delay, it's necessary to improve the hardware processing capability.
Modality attention fusion model with hybrid multi-head self-attention for video understanding

Video question answering (Video-QA) is a subject undergoing intense study in Artificial Intelligence, which is one of the tasks which can evaluate such AI abilities. In this paper, we propose a Modality Attention Fusion framework with Hybrid Multi-head Self-attention (MAF-HMS). MAF-HMS focuses on the task of answering multiple-choice questions regarding a video-subtitle-QA representation by fusion of attention and self-attention between each modality. We use BERT to extract text features, and use Faster R-CNN to ex-tract visual features to provide a useful input representation for our model to answer questions. In addition, we have constructed a Modality Attention Fusion (MAF) framework for the attention fusion matrix from different modalities (video, subtitles, QA), and use a Hybrid Multi-headed Self-attention (HMS) to further determine the correct answer. Experiments on three separate scene datasets show our overall model outperforms the baseline methods by a large margin. Finally, we conducted extensive ablation studies to verify the various components of the network and demonstrate the effectiveness and advantages of our method over existing methods through question type and required modality experimental results.
Key Principles of Governing Business-Incubator in University

Efficiency of economy development directly depends on implementation of measures supporting business entities, including upgrading entrepreneurial infrastructure. This problem was repeatedly discussed at the highest level of state governance of the Russian Federation, in particular within the frames of the President of Russian address to the Federal Assembly in 2021. Business-incubators are a key element of such infrastructure. It should be pointed out that specialized literature has not thoroughly studied the issue of business-incubators functions in today’s economy. In general these functions can be differentiated in accordance with the stage of macro-economic development and overall strategy of finance and economic policy observed within the frames of a concrete state, depending on geopolitical situation, trends in global economy changes, possible foreign economic preferences and risks. Effectiveness of today’s incubator development, viewed both in finance and social aspect directly depends on the quality of methodological tools of their functioning efficiency appraisal. The article gives a classification of business-incubators by a number of important economic and managerial criteria, which gives a chance to find their most preferable type depending on strategic goals and objectives of developing the incubator’s founder and general functioning requirements of the relative territorial (federal, regional or municipal) system of entrepreneurial work support. The author underlines that today’s business-incubator should possess a multi-sectoral character, it should be set up for the conventionally unlimited period, provide system and various finance support to residents , be integrated into space of digital economy, realize its own education programs of different profile and have a preventive model of management. Business-incubator is considered in two aspects. In a wider sense it is a specialized social and economic institution oriented to provision of long-term rise in intensity and finance and economic efficiency of entrepreneurial activity at the expense of favorable conditions for locating potentially competitive businessstructures at the initial stage of their life cycles on special territories. In the narrower sense business-incubator is an entity of territorial (federal, regional or municipal) system of supporting entrepreneurial initiatives, which could ensure their progressive development at the expense of providing production, technological, finance, information, consultative and other resources on preference or free of charge basis.
Construction of a Smart Supply Chain for Sand Factory Using the Edge-Computing-Based Deep Learning Algorithm

The diminishing natural sand has facilitated the booming of the sand manufacturing industry, and intelligent management of sand factories, in a time- and cost-efficient way, has become a growing tendency for the future. A role has been played in achieving intelligent management by constructing a smart supply chain. However, the smart sand factories are hardly involved in previously reported studies, which is inconsistent with related studies on smart factories and the Industrial Internet of Things (IIoT). In this paper, a smart supply chain management system (SSCMS) is constructed to realize the intelligence and automatization of the management of sand factories, using edge-computing and deep learning techniques. Along the supply chain, the deep learning model is used to realize the automatic identification of sand, avoiding the disadvantages of human identification, while improving the quality of sand factory operations. In order to relieve the pressure of network bandwidth, reduce system delay, and improve system operation efficiency, we use edge-computing technology to process data at the edge. To verify the performance of the constructed system, a sand factory simulation platform is established. Experiments show that the most critical indicator in the system, the accuracy rate of sand type identification, is above 98%, and the sand type identification time is only 0.022 s. In general, compared with traditional supply chain management, the constructed smart supply chain improves the quality and efficiency of sand factory operations, and all indicators of the designed system have achieved satisfactory results.
High payload watermarking based on enhanced image saliency detection

Abstract Nowadays, images are circulated rapidly over the internet and they are subject to some risk of misuses. To address this issue, various watermarking methods are proposed in the literature. However, most conventional methods achieve a certain trade-off among imperceptibility and high capacity payload, and they are not able to improve these criteria simultaneously. Therefore, in this paper, a robust saliency-based image watermarking method is proposed to achieve high payload and high quality watermarked image. First, an enhanced salient object model is proposed to produce a saliency map, followed by a binary mask to segments the foreground/background region of a host image. The same mask is then consulted to decompose the watermark image. Next, the RGB channels of the watermark are encrypted by using Arnold, 3-DES and multi-flipping permutation encoding (MFPE). Furthermore, the principal key used for encryption is embedded in the singular matrix of the blue channel. Moreover, the blue channel is encrypted by using the Okamoto-Uchiyama homomorphic encryption (OUHE) method. Finally, these encrypted watermark channels are diffused and embedded into the host channels. When the need arises, more watermarks can be embedded into the host at the expense of the quality of the embedded watermarks. Our method can embed watermark of the same dimension as the host image, which is the first of its kind. Experimental results suggest that the proposed method maintains robustness while achieving high image quality and high payload. It also outperforms the state-of-the-art (SOTA) methods.
Multi-objective Deep Data Generation with Correlated Property Control

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design. However, the advancement of deep generative models is limited by challenges to generate objects that possess multiple desired properties: 1) the existence of complex correlation among real-world properties is common but hard to identify; 2) controlling individual property enforces an implicit partially control of its correlated properties, which is difficult to model; 3) controlling multiple properties under various manners simultaneously is hard and under-explored. We address these challenges by proposing a novel deep generative framework that recovers semantics and the correlation of properties through disentangled latent vectors. The correlation is handled via an explainable mask pooling layer, and properties are precisely retained by generated objects via the mutual dependence between latent vectors and properties. Our generative model preserves properties of interest while handling correlation and conflicts of properties under a multi-objective optimization framework. The experiments demonstrate our model's superior performance in generating data with desired properties.
Application of hourglass matrix in Goldreich-Goldwasser-Halevi encryption scheme

Goldreich-Goldwasser-Halevi (GGH) encryption scheme is lattice-based cryptography with its security based on the shortest vector problem (SVP) and closest vector problem (CVP) with immunity to almost all attacks, including Shor's quantum algorithm and Nguyen's attack of higher lattice dimension. To improve the efficiency and security of the GGH Scheme by reducing the size of the public basis to be transmitted, we use an hourglass matrix obtained from quadrant interlocking factorization as a public key. The technique of quadrant interlocking factorization to yield a nonsingular hourglass matrix compensates the encryption scheme with better efficiency and security.
Financing Agriculture in Nigeria: A Comparative Review of Roles Played by Private, Public, and International Financial Institutions and Agencies

Financing agriculture in Nigeria from the perspective of both conventional and Islamic sources of finance has been faced with multi-dimensional challenges that impede the agricultural sector threatening sustainable agricultural production for food security.With a population size of 206 million and 70.8 million hectares of arable land, Nigeria is threatened with a high rate of unemployment, poverty, malnutrition, and social inequality.This can be related to the lack of access to finance by smallholder farmers in financial institutions.If access to financial institutions in critical sectors like agriculture and energy can be enhanced, then there will be an increased development in the overall value chain of the agricultural sector.This review paper is aimed at (1) Evaluating the current studies of the cause and effect by looking at the roles played by private, public, and international financial institutions and agencies and (2) Outlining the research gap in the literature with a focus on comparative study on financing agriculture in Nigeria.This study is based on a multi-dimensional theoretical approach (structural-functional, dependency theory, modernization theory, human needs theory, conflict and critical theories).The paper adopted the systematic literature review and reviewed 27 relevant source documents.Based on the review of the literature, the following major results emerged: Lack of macroeconomic policy, lack of access to finance, and bureaucratic bottleneck.The literature review revealed the challenges of financing agriculture and the effect on society based on results that emerged from the study.In addition, it also revealed the practical, evidence, theoretical, population, implementation, and methodological gaps that can lead to further research study.This comparative review study contributed to knowledge in expanding the existing literature on cause and effect by focusing on the roles played by private, public, and international financial institutions and agencies in financing agriculture using multi-dimensional theories and methods to find what the causes are, why they occur, what groups are affected, how they cope in the absence of funds, and what measures to apply to improve agricultural finance.
Intelligent Voice System Design for Optimizing E-Business Advertising Rhetoric Based on SVM Algorithm

With the emergence and development of artificial intelligence, design is no longer a process that can be completed only by creativity and knowledge. This article introduces research based on voice data mining and further discusses the optimization and application of rhetorical methods for e-commerce advertising. This article is working from two main aspects: one is to use association rule models in speech data mining to obtain useful rule relationships between Chinese prosody parameters, and the other is to use neural networks and data items in speech data mining. Using computer technology can make online advertisements interact with users. In addition to clicking on advertisements to jump to product pages, online advertisements also have multichannel sensory feelings, which can provide rich information and stimulate interest through interaction with users. We need to optimize the rhetoric of the advertisement and pay attention to the combination of the beauty of speech and the content of the advertisement when designing the advertisement. At the same time, we must also pay attention to the accuracy and elegance of the advertising language and wording.
Speeding-Up Elliptic Curve Cryptography Algorithms

In recent decades there has been an increasing interest in Elliptic curve cryptography (ECC) and, especially, the Elliptic Curve Digital Signature Algorithm (ECDSA) in practice. The rather recent developments of emergent technologies, such as blockchain and the Internet of Things (IoT), have motivated researchers and developers to construct new cryptographic hardware accelerators for ECDSA. Different types of optimizations (either platform dependent or algorithmic) were presented in the literature. In this context, we turn our attention to ECC and propose a new method for generating ECDSA moduli with a predetermined portion that allows one to double the speed of Barrett’s algorithm. Moreover, we take advantage of the advancements in the Artificial Intelligence (AI) field and bring forward an AI-based approach that enhances Schoof’s algorithm for finding the number of points on an elliptic curve in terms of implementation efficiency. Our results represent algorithmic speed-ups exceeding the current paradigm as we are also preoccupied by other particular security environments meeting the needs of governmental organizations.
Global Meets Local: Effective Multi-Label Image Classification via Category-Aware Weak Supervision

Multi-label image classification, which can be categorized into label-dependency and region-based methods, is a challenging problem due to the complex underlying object layouts. Although region-based methods are less likely to encounter issues with model generalizability than label-dependency methods, they often generate hundreds of meaningless or noisy proposals with non-discriminative information, and the contextual dependency among the localized regions is often ignored or over-simplified. This paper builds a unified framework to perform effective noisy-proposal suppression and to interact between global and local features for robust feature learning. Specifically, we propose category-aware weak supervision to concentrate on non-existent categories so as to provide deterministic information for local feature learning, restricting the local branch to focus on more high-quality regions of interest. Moreover, we develop a cross-granularity attention module to explore the complementary information between global and local features, which can build the high-order feature correlation containing not only global-to-local, but also local-to-local relations. Both advantages guarantee a boost in the performance of the whole network. Extensive experiments on two large-scale datasets (MS-COCO and VOC 2007) demonstrate that our framework achieves superior performance over state-of-the-art methods.
Empirical Study of Hate Speech in Social Media During COVID-19 Crisis in the United States

As the COVID-19 outbreak, hate speech on social media towards Chinese and other Asian groups has encouraged "Sinophobia". To capture the situation of hate speech on Twitter, we hydrated the tweets discussing COVID-19, written in English language and have location within the USA. These tweets hydrated from Twitter API in span of 5 months (153 days). We have obtained 543,943 tweets in which we identify 40,579 Hate Speech occurrences. We categorized and analyzed them according to Pysentimiento model which is based on BERT models and, Latent Dirichlet Allocation Model (LDA). The results indicate that there are substantial associations between the increased amount of hate speech and the increased rate of deaths due to COVID-19,increased rate of new COVID-19 cases, and negative tests rate.
Models of Computer Calculations

Introduction. The complexity of computational algorithms for solving typical problems of computational, applied, and discrete mathematics is analyzed from the perspective of the theory of computation, depending on the computer architecture and the used computing model: single-processor, multiprocessor, and quantum. The following classes of problems are considered: systems of linear algebraic equations, the Cauchy problem for systems of ordinary differential equations, numerical integration, boundary value problems for ordinary differential equations, factorization of numbers, finding the discrete logarithm of a number in multiplicative integer groups, searching for the necessary record in an unordered database, etc. The purposes of the paper are: 1. To investigate how the computational complexity depends on the computer architecture and the computational model. 2. To show that the construction of the computational process under the given conditions of calculations is related to the solution of the following problems: – the existence ε-solution to the problem; – the existence of T-effective computing algorithms; – the possibility of building a real computing process under the given computing conditions. 3. To investigate the effect of rounding numbers on computational complexity (especially when solving problems of transcomputational complexity). 4. To give the complexity estimates and total error of the computational algorithm for a number of typical problems of computational, applied, and discrete mathematics. The results. The complexity estimates of computational algorithms of the listed classes of problems for single-processor, multiprocessor and quantum computing models are given. The main focus is on high-performance computing: using the principles of parallel data processing and quantum mechanics. Conclusions. The connection of complexity estimates of computational algorithms with the architecture of computers and models of calculations is demonstrated. The characteristics of the first quantum computers (2016 – 2022), which have gone beyond laboratory research, are given. Keywords: computer technologies, rounding error, sequential, parallel and quantum computing models, complexity estimate.
Effects of illumination non-uniformity on the double-Ronchi lateral shearing interference field

Double-Ronchi shearing interferometry is a promising technique for insitu wavefront aberration measurement of the projection lens in photolithography systems. In practice, the non-uniformity of illumination is an important issue affecting the interference field, which has not been systematically researched. In this work, the interference field errors caused by non-uniform illumination distributions are analyzed utilizing the theories of scalar diffraction. The theoretical analysis has been verified by simulation and fundamental experiments. Results show that the uniformity requirements for the abrupt annular, Gaussian, and uniform random illumination distribution (RD) are 0.9434, 0.8439, and 0.2751, respectively, with a shear ratio of 5% and a relative wavefront reconstruction error of 1%. The uniformity of the three distributions is reduced to 0.6513, 0.5864, and 0.1234, respectively, with the shear ratio shrunk to 3%. When the shear ratio is less than 1%, there is no specific requirement for illumination uniformity.
Defensive Few-shot Learning

This paper investigates a new challenging problem called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">defensive few-shot learning</i> in order to learn a robust few-shot model against adversarial attacks. Simply applying the existing adversarial defense methods to few-shot learning cannot effectively solve this problem. This is because the commonly assumed sample-level distribution consistency between the training and test sets can no longer be met in the few-shot setting. To address this situation, we develop a general <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">defensive few-shot learning (DFSL) framework</i> to answer the following two key questions: (1) how to transfer adversarial defense knowledge from one sample distribution to another? (2) how to narrow the distribution gap between clean and adversarial examples under the few-shot setting? To answer the first question, we propose an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">episode-based adversarial training mechanism</i> by assuming a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">task-level distribution consistency</i> to better transfer the adversarial defense knowledge. As for the second question, within each few-shot task, we design two kinds of distribution consistency criteria to narrow the distribution gap between clean and adversarial examples from the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feature-wise</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">prediction-wise</i> perspectives, respectively. Extensive experiments demonstrate that the proposed framework can effectively make the existing few-shot models robust against adversarial attacks. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/WenbinLee/DefensiveFSL.git</uri> .
Teleportation in quantum edge networks (Conference Presentation)

The quantum internet will give us an infrastructure able to distribute and process quantum information on a planetary scale. The core of that internet will be formed from quantum error corrected links able to distribute information over large distances all while maintaining their coherence for long periods of time. However, many of the applications at the edge of such networks may rely on raw unencoded data – not protected by error correcting codes due to the nature of how it was generated. In this presentation, we will describe an experiment in which quantum information encoded on a physical qubit can be teleported into an error-corrected logical qubit. Our demonstration shows how one can get information into and out of quantum processors and tomorrows large-scale quantum networks.