Reviewerly/exHarmony huggingface dataset:
BTS: A Blockchain-Based Trust System to Deter Malicious Data Reporting in Intelligent Internet of Things

Recent developments in collection, computation and communication have expanded the way of data reporting in intelligent Internet of Things (IoT). However, diversity and complexity of data sources also impose new trust challenge in data collection process since untrust reporters tend to report false or even malicious data, which highlights the need to develop a novel methodology to solve such challenge. Thus, based on this domain, inspired by deterrence theory, this article proposes a blockchain-based trust system with assistant of drones to deter malicious data reporting in intelligent IoT. Specifically, to deter malicious data reporting, based on the blockchain technology, the data sensed by fully trusted drones is public published on blockchain showing participants the data standards, named as malicious deterrence scheme. This scheme provides a barrier for malicious reporters to arbitrarily publish false data to blockchain, since the false data can be easily detected while they cannot deny. Second, to further reduce malicious data reporting, a strict penalty mechanism is proposed to punish malicious reporters who have reported false data to blockchain to reduce the malicious data reporting in the following task through punishment. Third, note that the sensing of data standard generates additional costs, therefore, a drone flight route scheme based on a simper deep reinforcement learning with multihead attention mechanism (MA-DRL) is designed to reduce the flight distance for drones. Finally, extensive experiments demonstrate efficiency of our proposed system in terms of reducing malicious data reporting in advance as well as reducing drone flight distance.
Finances and National Economy: Frugal Economy as a Forced Approach of the COVID Pandemic

This paper aims to create an image both in terms of description and in terms of graphical representation, with multiannual coverage (2007–2019), the evolution of the financial indicators of the population, and their impact on the national economy. The main objective is to establish the basic pillars of the concentrated table of population financial indicators in terms of the values of absolute primary indicators, on the basis of which the values of the three relevant relative indicators that characterize the financial situation of the population were calculated directly for the potential and performance of the national economy, especially in the current context of the COVID-19 pandemic. The research methodology was based on a series of numerical data from public databases, tools, and appropriate research methods based on the calculation of primary indicators, and indicators derived by appropriate calculation formulas, for the identification of factors influencing the financial status of the population at and a comparative analysis of the financial assets and liabilities of households in Romania and the European Union. The results of the paper are given by the evolution of primary indicators on the characterization of the financial situation of the population in 2007–2019, especially for the active population, and are directly influenced by the quality of eligible employment and employed adults, as well as the sustainability of the national economy. The personal contribution of the research team consists of the mathematical correlations given by the population finances for the economy of a state, which are extremely relevant, especially since, depending on their positive or negative levels, systemic financial and social imbalances are created with direct impact on the sustainability of the national economy. Thus, we want to determine these financial indicators relevant to the economy. Additionally, due to the COVID-19 pandemic, the related issues to the population finances have aggravated and as such, there is a need for structural changes and adoptions. Finally, as an immediate and less costly solution, this study comes up with the suggestion of shifting the national economy toward frugality, therefore, significantly supporting sustainable development.
Domain adaptation with geometrical preservation and distribution alignment

Domain adaptation aims to learn a robust classifier for the target domain by leveraging knowledge from a different source domain. Existing methods realize the alignment of cross-domain distributions in manifold subspace to reduce the distribution divergence between the two domains. However, there exists a conspicuous deficiency in them, i.e., the exploration of preserving statistical and geometrical properties simultaneously is still under insufficient, which, to some extent, would cause the under adaptation effect. The statistical and geometrical properties play an important role in minimizing the domain discrepancy underlying the joint probability distributions. For better and adequately exploiting the statistical and geometrical properties, we propose a novel feature adaptation method in this paper, called domain adaptation with geometrical preservation and distribution alignment (GPDA). Specifically, GPDA performs graph dual regularization in the nonnegative matrix factorization framework with label constraints, to learn the discriminative and domain-invariant features while preserving both the statistical properties and geometrical structures of the original data, such that the cross-domain difference can be effectively and positively narrowed. Meanwhile, GPDA simultaneously aligns the marginal and conditional probability distributions in the nonnegative matrix factorization framework during the learning of domain-invariant features, to further minimize the domain gap between the source and target domains, which can adequately transfer knowledge from the source domain to the target domain. Extensive experiments on seven benchmark datasets demonstrate the effectiveness of the proposed GPDA algorithm in cross-domain image classification.
An empirical analysis of the information security culture key factors framework

Information security is a challenge facing organisations, as security breaches pose a serious threat to sensitive information. Organisations face security risks in relation to their information assets, which may also stem from their own employees. Organisations need to focus on employee behaviour to limit security failures, as if they wish to establish effective security culture with employees acting as a natural safeguard for information assets. This study was conducted to respond to a need for more empirical studies that focus on a development of security culture to provide a comprehensive framework. The Information Security Culture and Key Factors Framework has been developed, incorporating two types of factors: those that influence security culture and those that reflect it. This paper validates the applicability of the framework and tests related hypotheses through an empirical study. An exploratory survey was conducted, and 266 valid responses were obtained. Phase two of the study demonstrates the framework levels of validity and reliability through the use of factor analysis. Different hypothetical correlations were analysed through the use of structural equation modelling, with indirect exploratory effect of the moderators achieved through a multi-group analysis. The findings show that the framework has validity and achieved an acceptable fit with the data. This study fills an important gap in the significant relationship between personality traits and security culture. It also contributes to the improvement of information security management through the introduction of a comprehensive framework in practice, which functions in the establishment of security culture. The factors are vital in justifying security culture acceptance, and the framework provides an important tool that can be used to assess and improve an organisational security culture.
Sentiment Analysis on COVID Tweets: An Experimental Analysis on the Impact of Count Vectorizer and TF-IDF on Sentiment Predictions using Deep Learning Models

Due to the higher popularity of social media and its excessive use, COVID-19 has become the topic of the talk since 2019 and it has become a cause of stress, anxiety and depression for people around the world. In this article, we experimented with different classifiers on COVID data to train deep neural networks to enhance the accuracy rate using two popular word embedding techniques: Count Vectorizer and Term Frequency-Inverse Document Frequency. Finally, we compare accuracies and observe that TF-IDF comes out to be more efficient as compared to Count Vectorizer where datasets are of huge volume and in our case i.e., for covid19 tweets, both vectorizers have been approximately similar in performance except on Single Layer Perceptron where Count Vectorizer results in 10% more efficiency in terms of accuracy.
Low-Altitude Remote Sensing Opium Poppy Image Detection Based on Modified YOLOv3

Poppy is a special medicinal plant. Its cultivation requires legal approval and strict supervision. Unauthorized cultivation of opium poppy is forbidden. Low-altitude inspection of poppy illegal cultivation through unmanned aerial vehicle is featured with the advantages of time-saving and high efficiency. However, a large amount of inspection image data collected need to be manually screened and analyzed. This process not only consumes a lot of manpower and material resources, but is also subjected to omissions and errors. In response to such a problem, this paper proposed an inspection method by adding a larger-scale detection box on the basis of the original YOLOv3 algorithm to improve the accuracy of small target detection. Specifically, ResNeXt group convolution was utilized to reduce the number of model parameters, and an ASPP module was added before the small-scale detection box to improve the model’s ability to extract local features and obtain contextual information. The test results on a self-created dataset showed that: the mAP (mean average precision) indicator of the Global Multiscale-YOLOv3 model was 0.44% higher than that of the YOLOv3 (MobileNet) algorithm; the total number of parameters of the proposed model was only 13.75% of that of the original YOLOv3 model and 35.04% of that of the lightweight network YOLOv3 (MobileNet). Overall, the Global Multiscale-YOLOv3 model had a reduced number of parameters and increased recognition accuracy. It provides technical support for the rapid and accurate image processing in low-altitude remote sensing poppy inspection.
Searching for Fast Model Families on Datacenter Accelerators

Neural Architecture Search (NAS), together with model scaling, has shown remarkable progress in designing high accuracy and fast convolutional architecture families. However, as neither NAS nor model scaling considers sufficient hardware architecture details, they do not take full advantage of the emerging datacenter (DC) accelerators. In this paper, we search for fast and accurate CNN model families for efficient inference on DC accelerators. We first analyze DC accelerators and find that existing CNNs suffer from insufficient operational intensity, parallelism, and execution efficiency and exhibit FLOPs-latency nonproportionality. These insights let us create a DC-accelerator-optimized search space, with space-to-depth, space-to-batch, hybrid fused convolution structures with vanilla and depthwise convolutions, and block-wise activation functions. We further propose a latency-aware compound scaling (LACS), the first multi-objective compound scaling method optimizing both accuracy and latency. Our LACS discovers that network depth should grow much faster than image size and network width, which is quite different from the observations from previous compound scaling. With the new search space and LACS, our search and scaling on datacenter accelerators results in a new model series named EfficientNet-X. EfficientNet-X is up to more than 2X faster than Efficient-Net (a model series with state-of-the-art trade-off on FLOPs and accuracy) on TPUv3 and GPUv100, with comparable accuracy. EfficientNet-X is also up to 7X faster than recent RegNet and ResNeSt on TPUv3 and GPUv100. Source code is at https://github.com/tensorflow/tpu/tree/master/models/official/efficientnet/tpu
Research on IT English Flipped Classroom Teaching Model Based on SPOC

In order to improve the existing problems in the teaching process of IT English courses and improve the quality of IT English, this paper conducts a research on the flipped classroom teaching mode of IT English based on SPOC. The present situation of IT English teaching is analyzed, and the problems existing in the teaching process are also analyzed. On the basis of the above thought, with the support of SPOC platform build IT turn English classroom teaching mode, namely, by setting the course target, learning for class in advance, choose high-quality class, learning information collection, upload the related resources, and do a good job in teaching design complete teacher preparation, and design the specific teaching unit of teaching process, teaching quality evaluation model was constructed. In this way, the teaching quality evaluation results of IT English flipped classroom are obtained, in order to further improve the teaching quality. The experimental results show that the flipped English teaching mode based on SPOC can effectively improve students’ performance and increase students’ average daily learning time and course satisfaction, and the practical application effect is good.
An Adaptive Formulation of the Sliding Innovation Filter

In this paper, an adaptive formulation of the sliding innovation filter (SIF) is presented. The SIF is a recently proposed estimation strategy that has demonstrated robustness to modeling errors and uncertainties. It utilizes a switching gain that is a function of the innovation (measurement error) and sliding boundary layer term. In this paper, a time-varying sliding boundary layer is derived based on minimizing the state error covariance. The resulting solution creates an adaptive formulation of the SIF. The adaptive SIF is applied on a linear aerospace system, and is compared with the well-known Kalman filter (KF) and the standard SIF. The results demonstrate the robustness of the new estimation strategy in the presence of modeling uncertainties and system faults.
Explainable Fuzzy Utility Mining on Sequences

Fuzzy systems have good modeling capabilities in several data science scenarios and can provide human-explainable intelligence models with explainability and interpretability. To obtain a human-explainable data intelligence model for decision making, in this article, we investigate explainable fuzzy-theoretic utility mining on multisequences. Meanwhile, a more normative formulation of the problem of fuzzy utility mining on sequences is formulated. By exploring fuzzy set theory for utility mining, we propose a novel method termed pattern growth fuzzy utility mining (PGFUM) for mining fuzzy high-utility sequences with linguistic meaning. In the case of sequence data, PGFUM reflects the fuzzy quantity and utility regions of sequences. To improve the efficiency and feasibility of PGFUM, we develop two compressed data structures with explainable fuzziness. Furthermore, one existing and two new upper bounds on the explainable fuzzy utility of candidates are adopted in three proposed pruning strategies to substantially reduce the search space and, thus, expedite the mining process. It is demonstrated that PGFUM achieves not only human-explainable mining results that contain the original nature of revealable intelligibility, but also high efficiency in terms of runtime and memory cost.
A bibliometric analysis of artificial intelligence publications from 1991 to 2018

This study aimed to analyze the characteristics of artificial intelligence-related publications in Science Citation Index Expanded (SCIEXPANDED) from 1991 to 2018. The analyzed aspects covered distribution of annual publications, citations per publication, journals, Web of Science categories, countries, institutions, as well as research foci and their trends. A total of 13,251 artificial intelligence-related articles were found. Articles were published in a wide range of journals and Web of Science categories. The United States took the lead position in total, single country, international collaboration, and first, corresponding, and single author articles as well as citations per publication among 119 countries. Chinese Academy of Sciences in China, Islamic Azad University in Iran, and Massachusetts Institute of Technology (MIT) in USA were the three most productive institutions. MIT had higher citations per publication. An international collaborative article by authors from Canada, the United States, and Switzerland was the most frequently cited article with the most total citations from Web of Science Core Collection since publication through the end of 2018. Results from word cluster analysis showed that models, neural networks, learning, and prediction were the most popular topics and features, classification, and optimization might be focus in artificial intelligence research.
PAR-GAN: Improving the Generalization of Generative Adversarial Networks Against Membership Inference Attacks

Recent works have shown that Generative Adversarial Networks (GANs) may generalize poorly and thus are vulnerable to privacy attacks. In this paper, we seek to improve the generalization of GANs from a perspective of privacy protection, specifically in terms of defending against the membership inference attack (MIA) which aims to infer whether a particular sample was used for model training. We design a GAN framework, partition GAN (PAR-GAN), which consists of one generator and multiple discriminators trained over disjoint partitions of the training data. The key idea of PAR-GAN is to reduce the generalization gap by approximating a mixture distribution of all partitions of the training data. Our theoretical analysis shows that PAR-GAN can achieve global optimality just like the original GAN. Our experimental results on simulated data and multiple popular datasets demonstrate that PAR-GAN can improve the generalization of GANs while mitigating information leakage induced by MIA.
Guided Interactive Video Object Segmentation Using Reliability-Based Attention Maps

We propose a novel guided interactive segmentation (GIS) algorithm for video objects to improve the segmentation accuracy and reduce the interaction time. First, we design the reliability-based attention module to analyze the reliability of multiple annotated frames. Second, we develop the intersection-aware propagation module to propagate segmentation results to neighboring frames. Third, we introduce the GIS mechanism for a user to select unsatisfactory frames quickly with less effort. Experimental results demonstrate that the proposed algorithm provides more accurate segmentation results at a faster speed than conventional algorithms. Codes are available at https://github.com/yuk6heo/GIS-RAmap.
Application of Blockchain-Based Sports Health Data Collection System in the Development of Sports Industry

In recent years, with the continuous development of blockchain technology and the prosperity of digital currency, various industries have carried out extensive practice around the application of blockchain technology, such as my country’s sports industry. Through the measurement and collection of athletes’ health and exercise information, the athlete’s exercise volume and exercise level can be quantitatively evaluated. Blockchain technology provides a new opportunity for the development of the sports industry. The characteristics of the decentralization, security, credibility, and traceability of the data collection system are highly compatible with the development of the sports industry. In order to develop and expand the sports industry and scientifically evaluate the physical fitness of athletes, this research focuses on the blockchain-based sports health data collection system in the sports industry. This article adopts the accuracy index and the formula of data return and conducts demand analysis, accuracy, and recall analysis of the health data collection system. The research conclusion of this paper is that when using sensors to collect sports health data, the data collection of sensors located on the arm is more accurate, with an accuracy rate of over 90%, and the collection effect of multinode sensors is higher than that of single-node sensors. The collection of sports health data is beneficial to the training of athletes and the development of the sports industry to meet the growing demand for sports health. Our country’s sports industry will also develop with the application of blockchain.
Self-Supervised Learning for Human Pose Estimation in Sports

Human pose estimation (HPE) is a commonly used technique to determine derived parameters that are important to improve the performance of athletes in many sports disciplines. This paper proposes two methods to fine-tune a HPE system trained on general poses to a sports discipline specific HPE model using only a few labeled images. We show that 50 labeled 2D poses and additionally unlabeled videos are sufficient to achieve a Percentage of Correct Keypoints (PCK) of 88.6% at a threshold of 0.1 in the disciplines of triple and long jump, closing the gap between the supervised fine-tuning on the same 50 images and the fully supervised training on $60 \times$ more images by 60%. The first proposed method uses pseudo labels as a self-supervised training technique together with a filtering method of the pseudo labels. Furthermore, this paper shows that a mean teacher approach, which is based on consistency between a teacher and a student model, can also improve the results.
Mining Better Samples for Contrastive Learning of Temporal Correspondence

We present a novel framework for contrastive learning of pixel-level representation using only unlabeled video. Without the need of ground-truth annotation, our method is capable of collecting well-defined positive correspondences by measuring their confidences and well-defined negative ones by appropriately adjusting their hardness during training. This allows us to suppress the adverse impact of ambiguous matches and prevent a trivial solution from being yielded by too hard or too easy negative samples. To accomplish this, we incorporate three different criteria that ranges from a pixel-level matching confidence to a video-level one into a bottom-up pipeline, and plan a curriculum that is aware of current representation power for the adaptive hardness of negative samples during training. With the proposed method, state-of-the-art performance is attained over the latest approaches on several video label propagation tasks.
An Empirical Study of Vehicle Re-Identification on the AI City Challenge

This paper introduces our solution for the Track2 in AI City Challenge 2021 (AICITY21). The Track2 is a vehicle re-identification (ReID) task with both the real-world data and synthetic data. We mainly focus on four points, i.e. training data, unsupervised domain-adaptive (UDA) training, post-processing, model ensembling in this challenge. (1) Both cropping training data and using synthetic data can help the model learn more discriminative features. (2) Since there is a new scenario in the test set that dose not appear in the training set, UDA methods perform well in the challenge. (3) Post-processing techniques including re-ranking, image-to-track retrieval, inter-camera fusion, etc, significantly improve final performance. (4) We ensemble CNN-based models and transformer-based models which provide different representation diversity. With aforementioned techniques, our method finally achieves 0.7445 mAP score, yielding the first place in the competition. Codes are available at https://github.com/michuanhaohao/AICITY2021_Track2_DMT.