Reviewerly/exHarmony huggingface dataset:
Multi-Order Feature Statistical Model for Fine-Grained Visual Categorization

Fine-grained visual categorization aims to learn a robust image representation modeling subtle differences from similar categories. Existing methods in this field tackle the problem by designing complex frameworks, which produce high-level features by performing first-order or second-order pooling. Despite the impressive performance achieved by these strategies, the single-order networks only carry linear or non-linear information of the last convolutional layer, neglecting the fact that features from different orders are mutually complementary. In this paper, we propose a multi-order feature statistical method (MOFS), which learns fine-grained features characterizing multiple orders. Specifically, the MOFS consists of two sub-modules: (i) a first-order module modeling both mid-level and high-level features. (ii) a covariance feature statistical module capturing high-order features. By deploying these two sub-modules on the top of existing backbone networks, MOFS simultaneously captures multi-level of discriminative patters including local, global and co-related patters. We evaluate the proposed method on three challenging benchmarks, namely CUB-200-2011, Stanford Cars, and FGVC-Aircraft. Compared with state-of-the-art methods, experiment results exhibit superior performance in recognizing fine-grained objects.
Can Blockchain Revolutionize Tax Administration?

Experts predict that the use of smart contracts and other applications of blockchain technology can potentially revolutionize the manner in which we do business. Blockchain promises the elimination of middlemen, as well as trust, transparency, and improved access to shared information and records. Thus, it is no surprise that companies and entrepreneurs are now developing blockchain solutions for an array of markets, ranging from real estate to health care. But, can this new technology revolutionize tax administration? Our current tax administration system suffers from a large tax gap, high compliance and administrative costs, and many inefficiencies. Blockchain’s core attributes may present a solution to these shortcomings. This Article is the first to consider the technology’s potential role in revolutionizing tax administration and the challenges that must be overcome before incorporating blockchain technology into the tax space. The Article demonstrates that implementing a blockchain-based platform for tax administration would present significant opportunities to digitalize and automate certain tax processes, minimize government information constraints, increase the transparency and trustworthiness of tax-related transactions, and reduce costs, data redundancies, and other inefficiencies involved in the tax administration process. The Article concludes, however, that many blockchain tax initiatives are merely aspirational at this point. Governments need to overcome significant challenges and limitations in order to meaningfully take advantage of blockchain technology. It, therefore, sets forth normative steps for policymakers to take in supporting the development of blockchain technology and helping it to realize its full potential in the tax space. By doing so, the Article aims to promote a proactive approach to exploring and understanding the technology’s benefits, limitations and implications, and thereby to place the government in the best position to harness the advantages of blockchain technology and modernize our system of tax administration.
Moving Object Tracking via 3-D Total Variation in Remote-Sensing Videos

Tracking moving objects in remote-sensing videos is becoming increasingly important in remote-sensing analysis. This letter presents a novel object tracking method for remote-sensing videos. We start with using the traditional robust principal component analysis (RPCA) model to extract the moving object from the background. To describe the continuity of moving objects in spatial and temporal directions, we incorporate a 3-D total variation (3DTV) regularization into the RPCA model. Considering that the background is not static and the captured videos will contain noise because of the instability of the sensing camera, our proposed method introduces a certain part of the function to model the noise and capture the changes in background. Experimental results on real videos provided by 2016 IEEE GRSS Data Fusion Contest and 2020 Hyperspectral Object Tracking Challenge demonstrate the advantages of the moving object-tracking method via 3-D TV.
Research on school education reform under the background of "5G + education"

With the continuous development of science and technology, mankind has entered the 5G era. In this background, the development of many fields will face great opportunities and challenges, including the field of education. The previous teaching mode, method and content are difficult to adapt to the development needs of 5G era. Therefore, it is of great significance to study the school teaching reform in the era of "5G + education". Based on this, this paper first analyzes the challenges and opportunities brought by the 5G era to school education, and then studies the school education reform in the era of "5G + education", hoping to provide some reference and help for the reform and development of school education in the era of "5G + education".
LCC-Net: A Lightweight Cross-Consistency Network for Semisupervised Cardiac MR Image Segmentation

Semantic segmentation plays a crucial role in cardiac magnetic resonance (MR) image analysis. Although supervised deep learning methods have made significant performance improvements, they highly rely on a large amount of pixel-wise annotated data, which are often unavailable in clinical practices. Besides, top-performing methods usually have a vast number of parameters, which result in high computation complexity for model training and testing. This study addresses cardiac image segmentation in scenarios where few labeled data are available with a lightweight cross-consistency network named LCC-Net. Specifically, to reduce the risk of overfitting on small labeled datasets, we substitute computationally intensive standard convolutions with a lightweight module. To leverage plenty of unlabeled data, we introduce extreme consistency learning, which enforces equivariant constraints on the predictions of different perturbed versions of the input image. Cutting and mixing different training images, as an extreme perturbation on both the labeled and unlabeled data, are utilized to enhance the robust representation learning. Extensive comparisons demonstrate that the proposed model shows promising performance with high annotation- and computation-efficiency. With only two annotated subjects for model training, the LCC-Net obtains a performance gain of 14.4% in the mean Dice over the baseline U-Net trained from scratch.
Price Suggestion for Online Second-hand Items

This paper describes an intelligent price suggestion system for online second-hand listings. In contrast to conventional pricing strategies which are employed to a large number of identical products, or to non-identical but similar products such as homes on Airbnb, the proposed system provides price suggestions for online second-hand items which are non-identical and fall into numerous different categories. Moreover, simplifying the item listing process for users is taken into consideration when designing the price suggestion system. Specifically, we design a truncate loss to train a vision-based price suggestion module which mainly takes some vision-based features as input to first classify whether an uploaded item image is qualified for price suggestion, and then offer price suggestions for items with qualified images. For the items with unqualified images, we encourage users to input some text descriptions of the items, and with the text descriptions, we design a multimodal item retrieval module to offer price suggestions. Extensive experiments demonstrate the effectiveness of the proposed system.
Efficient Load Optimization Method Using VM Migration in Cloud Environment

Virtualization is the fundamental integral part of CLOUD technology where load balancing among virtual machines (VM) and physical machines (PM) are managed. Heavy workload in PM makes cloud more imbalance and unorganized. To handle load balancing among PMs, VM migration is one of the best approaches. In this paper, a new approach is introduced to explore the best optimal destination PM for VM migration using soft computing based Type2 Fuzzy Logic technique (T2FL). The VM which has minimum migration time gets selected for migration from source PM to optimal destination PM. The simulation is performed using CloudSim tool and java-based fuzzy (Juzzy) packages have been extended for fuzzy system results. The simulated results of proposed VMMLB-FLS has been analyzed with existing Round Robin and CM-eFCFS cloud-based algorithms with respect to performance metrics Transmission Time, Execution Time, MakeSpan, and Number of Migrations. The proposed method results depict the effectiveness of VMMLB-FLS compared to existing methods.
Gadgets and Messenger Applications as an Instrument for Secondary School Administration

This research is intended to study the influence that gadgets and messenger applications have on the management process in institutions of general secondary education (IGSE) in Ukraine. The relevance thereof is conditioned by the fact that this subject matter has never been previously in the focus of any specific research in either Ukraine or the world. It relies on the data collected through an online questionnaire distributed among the IGSE administrators of Ukraine. The findings reveal that the use of gadgets and messenger applications in IGSEs is becoming new normal in the management activity of the IGSE administrators while raising a number of ethical questions related to violation of the subordinates’ rights to privacy and personal time and space.
Disruption, digital literacy and UK Higher Education Policy

Higher education (HE) in the United Kingdom (UK) has been on a trajectory of marketisation for much of the 21st Century (see Marginson, 2018) These developments reflect international policy trends toward a more pervasive neoliberal ideology that has sought to transform the global HE sector into a capitalist market subject to buyer-seller relations (Antonucci, 2016) In the UK, however, these policy directions have not yet led to a 'free market' in the way that might have otherwise been envisioned At the same time, Higher Education Institutions (HEIs) have also seen wide ranging developments in the use of information communication technologies (ICTs) This has, as Flavin notes, been most notable in respect to the almost universal imposition of Virtual Environments (VLEs) to support learning and teaching, increase access and retention, and reduce costs Within all of this 'cost-sharing' and 'digital development', it would be difficult to argue against Flavin's suggestion that digital technology has, at best, enhanced rather than disrupted HE markets -- and certainly VLEs have tended to be subsumed into practice rather than necessarily changing it As his paper highlights, this has often meant that VLEs are little more than facilities for storage rather than a sustained multimedia means for delivery, interaction, and assessment But Flavin's discussion of 'disruptive innovation' also raises the interesting possibility that this might not be the case following the end of the COVID-19 pandemic Indeed, the idea that the crisis will create opportunities for existing universities to rethink teaching delivery in relation to digital technologies is a provocative one -- and it is one that has already been mooted by universities themselves (Jisc, 2020) Of course, there is a well established literature on the use of VLEs within Higher Education Institutions and it would be impossible to cover all of that material in this response (see Davies et al , 2017 for a brief review) But it is worth further exploring one particular reason why VLEs often fail to fulfil their promise, because it might help better assess whether the pandemic could provoke the Disruption that is imagined by Flavin and others: and that one reason is the issue of digital literacy [This article reflects on Michael Flavin's article, Disruptive Innovation and Technology Enhanced Learning (EJ1288163) ]
Blockchain in the “Time Bank”: Toward a Community-Oriented Public Interest Technology

Blockchain technology has been popularized in recent years, fundamentally affecting the way people will trade and exchange in the future. As people exchange services and skills in “time banks” with reciprocity as the premise, it seems a community system can be developed using blockchain technology. This study aims to explore how to apply blockchain technology in time banks; it describes how time banks' roles and functions will change once blockchain technology is adopted in. The proposed blockchain solution for time banks will supply a meaningful reference for government policy makers, nonprofit organizations, social workers, communities, and researchers. Blockchain will thus find its own way in the complex social welfare field and contribute greater value to our society.
The trends for modern libraries and building the strategy of library and information education in the Republic of Kazakhstan

The modern trends for the libraries to update library and information education in the Republic of Kazakhstan are examined. The state and the society offer challenges: meeting the expanding information demands in science, education, economics and industries; contributing to the information society. The human resources in the information industry makes the most important factor of increasing the efficiency of libraries. In 2018, the Congress of Kazakhstan librarians adopted the concept of human capital assets which required advanced training, expanding professional competencies and recruiting young professionals. Today, the university library departments focus on increasing scientific and creative potential of the faculty, engaging students in projects and creative activities; educating creative thinking and skills of applying innovative technologies in professional activities. The educational process and curricula are improved as a result of the feedback obtained from graduates and employers. Among the modernization trends in library education in the Republic of Kazakhstan are the country’s integration into the single information space and implementation of the main educational content within the context of trilingual educational policy.
Influence and Application of Big Data Analysis in Product Design Research

Abstract Design research is an important work in product design. Through a series of data collection and data analysis, we can better adjust the design scheme. The traditional product design research mode data analysis effect is not good, through the Big Data(BD) technology research and analysis of product design, is a good solution at present. Therefore, this paper establishes the research on the influence and application of BD analysis in product design research. In this paper, the product design research and the application of BD analysis technology in the design research are deeply studied and analyzed. It is believed that through the BD technology, the research content can be analyzed more scientifically, so as to improve the overall product design effect. In order to make the BD analysis technology better applied in product design research, this paper optimizes and improves the evaluation crowd weight calculation method of entropy processing, which effectively improves the accuracy of the algorithm. In order to further verify the application effect of BD analysis in product design research; this paper establishes a test experiment with “mobile power supply” as the product. The experimental results show that among the tested users, young people aged 15-20 are the main ones, accounting for 38.7%. According to the analysis of users’ interests and hobbies, the highest proportion of digital options is 31.2%. The analysis shows that the results of BD analysis provide valuable reference for the formulation and adjustment of product design strategy.
Webly Supervised Deep Attentive Quantization

Learning to hash has been widely applied in large-scale image retrieval. Although current deep hashing methods yield state-of-the-art performance, their heavy dependence on groundtruth information actually makes it difficult to deploy in practical applications such as social media. To solve this problem, we propose a novel method termed Webly Supervised Deep Attentive Quantization (WSDAQ), where deep quantization is trained on web images associated with some userprovided weak tags, without consulting any ground-truth labels. Specifically, we design a tag processing module to leverage semantic information of tags so as to better supervised quantization learning. Besides, we propose an end-to-end trainable Attentive Product Quantization Module (APQM) to quantize deep features of images. Furthermore, we use a noise-contrastive estimation loss to train the model from the perspective of contrastive learning. Experiments validate that WSDAQ is superior to state-of-the-art baselines in compact coding trained on weakly-tagged web images.
A-Visor and A-Camera: Arduino-based Cardboard Head-Mounted Controllers for VR Games

The Nintendo Labo: VR KitTM introduced several types of cardboard controllers that allow users to enjoy virtual reality through various interactions. However, it is not compatible with smartphone devices which many people can use to access VR easily. In this study, we used Arduino and a smartphone to create two customized low-cost cardboard head-mounted VR controllers which we called A-Visor and A-Camera. We also created VR games for AVisor and A-Camera using Unity3D. Thus, we present new DIY head-mounted VR controllers that are made by assembling corrugated cardboard materials, Arduino, and sensors, which are readily accessible to DIY enthusiasts.
Asynchronous Delayed Optimization With Time-Varying Minibatches

Large-scale learning and optimization problems are often solved in parallel. In a master-worker distributed setup, worker nodes are most often assigned fixed-sized minibatches of data points to process. However, workers may take different amounts of time to complete their per-batch calculations. To deal with such variability in processing times, an alternative approach has recently been proposed wherein each worker is assigned a fixed duration to complete the calculations associated with each batch. This fixed-time approach results in time-varying minibatch sizes and has been shown to outperform the fixed minibatch approach in synchronous optimization. In this paper we make a number of contributions in the analysis and experimental verification of such systems. First, we formally present a system model of an asynchronous optimization scheme with variable-sized minibatches and derive the expected minibatch size. Second, we show that for our fixed-time asynchronous approach, the expected gradient staleness does not depend on the number of workers contrary to existing schemes. Third, we prove that for convex smooth objective functions the asynchronous variable minibatch method achieves the optimal regret and optimality gap bounds. Finally, we run experiments comparing the performances of the asynchronous fixed-time and fixed-minibatch methods. We present results for CIFAR-10 and ImageNet.
Index Design of Electronic Medical Record Database Using Blockchain

The Electronic Medical Record (EMR) is a kind of data, which is highly sensitive and worthy of biomedical research. The storage security, integrity, and data sharing of EMRs need further research and discussion. Blockchain is a block-chain storage structure, which has the characteristics of tamper-proof, traceability, and high sharing. Although blockchain technology provides a new solution for the storage and data sharing of EMRs, the research on user-oriented data query is not comprehensive. Traditional SQL database opens its data access, so the data stored in it has easily been tampered with, which is not suitable for storing sensitive medical data. Based on the characteristics of blockchain and traditional databases, we design a model with high security and easy retrieval for the storing and querying the EMRs. In the case of ensuring the safety of medical data storage, it is convenient for users to query and retrieve.
Joint Learning of Image Aesthetic Quality Assessment and Semantic Recognition Based on Feature Enhancement

Aesthetic quality assessment and semantic recognition are the two fundamental aspects of image perception and understanding tasks. Though these two tasks are related, most of the current research generally treats them as independent problems without any interaction. In this paper, we explore the relationships between aesthetic quality assessment and semantic recognition task, and employ a multi-task convolutional neural network with feature enhancement mechanism to effectively integrate these two tasks. A novel Enhanced Aggregation of Features Network (EAFNet) for joint learning of the two tasks is proposed to enhance the valid features and suppress the invalid features of each task in both channel and spatial dimensions. Experiments conducted on two benchmark datasets well verify the superior performance of EAFNet in handling aesthetic quality assessment and semantic recognition tasks.
A General Model for Learning Node and Graph Representations Jointly

This paper focuses on two fundamental graph recognition tasks: node classification and graph classification. Existing methods usually learn the node and graph representations for these two tasks separately, and ignore modeling the relations between the local and global structures. In this paper, we propose a general approach to learn the local and global features collaboratively: (1) in order to characterize the correlation among nodes and communities (a set of nodes), we employ the joint community detection/dynamic routing modules to generate the clustering assignment matrices at first and then utilize these matrices to cluster nodes to capture the global information of graphs (locally relevant graph representations). Inspired by the success of spectral clustering, we minimize the ratiocut loss to help optimize the learned assignment matrices. (2) We maximize the mutual information between local and global representations to help learn the globally relevant node representations. Experimental results on a variety of node and graph classification benchmarks show that our model can achieve superior performance over the state-of-the-art approaches.