Reviewerly/exHarmony huggingface dataset:
Influence of sand and dust turbulent atmosphere on performance of free space quantum communication

Quantum communication is a frontier hotspot of current research, and it has ideal information security. In order to enable quantum systems in arid and desertified areas to work almost under all-weather condition, it is necessary to study the attenuation of free-space quantum signal transmission and the influence of the turbulence atmosphere carrying sand and dust on communication performance. Using Mie scattering theory, multiple scattering simulation method, and atmospheric turbulence theory, the attenuation of optical wave transmission in sand and dust turbulent atmospheric channels with different visibility, and the influence of multiple scattering and turbulence on attenuation are studied. The results show that the effect of multiple scattering increases with the decrease of visibility, the turbulence effect gradually strengthens with the increase of distance. According to the quantum amplitude damped channel model, the effects of multiple scattering and turbulence in the sand and dust turbulent atmosphere with different visibility on the quantum channel capacity, fidelity and bit error rate are analyzed. The results show that as the visibility decreases, the multiple scattering effect increases, resulting in the decrease of attenuation and bit error rate, but an increase in channel capacity, fidelity and the boundaries of security key rate. The existence of turbulence in the dust atmosphere will increase the attenuation and bit error rate, but reduce the channel capacity, fidelity and security key rate. It can be seen that the influence of multiple scattering and turbulence on the communication performance, when the visibility of the sand and dust atmosphere are both low, cannot be ignored. In practical applications, the relevant parameters of quantum communication should be adaptively adjusted according to the visibility and turbulence intensity to improve the probability, efficiency and reliability of quantum communication.
Debiasing the Cloze Task in Sequential Recommendation with Bidirectional Transformers

Bidirectional Transformer architectures are state-of-the-art sequential recommendation models that use a bi-directional representation capacity based on the Cloze task, a.k.a. Masked Language Modeling. The latter aims to predict randomly masked items within the sequence. Because they assume that the true interacted item is the most relevant one, an exposure bias results, where non-interacted items with low exposure propensities are assumed to be irrelevant. The most common approach to mitigating exposure bias in recommendation has been Inverse Propensity Scoring (IPS), which consists of down-weighting the interacted predictions in the loss function in proportion to their propensities of exposure, yielding a theoretically unbiased learning. In this work, we argue and prove that IPS does not extend to sequential recommendation because it fails to account for the temporal nature of the problem. We then propose a novel propensity scoring mechanism, which can theoretically debias the Cloze task in sequential recommendation. Finally we empirically demonstrate the debiasing capabilities of our proposed approach and its robustness to the severity of exposure bias.
Toward Graph Minimally-Supervised Learning

To model graph-structured data, graph learning, in particular deep graph learning with graph neural networks, has drawn much attention in both academic and industrial communities lately. The effectiveness of prevailing graph learning methods usually rely on abundant labeled data for model training. However, it is common that graphs are scarcely labeled since data annotation and labeling on graphs is always time and resource-consuming. Therefore, it is imperative to investigate graph learning with minimal human supervision for the low-resource settings where limited or even no labeled data is available. In this tutorial, we will focus on the state-of-the-art techniques of Graph Minimally-Supervised Learning, in particular a series of weakly-supervised learning, few-shot learning, and self-supervised learning methods on graph-structured data as well as their real-world applications. The objectives of this tutorial are to: (1) formally categorize the problems in graph minimally-supervised learning and discuss the challenges under different learning scenarios; (2) comprehensively review the existing and recent advances of graph minimally-supervised learning; and (3) elucidate open questions and future research directions. This tutorial introduces major topics within minimally-supervised learning and offers a guide to a new frontier of graph learning.
Sufficient Vision Transformer

Currently, Vision Transformer (ViT) and its variants have demonstrated promising performance on various computer vision tasks. Nevertheless, task-irrelevant information such as background nuisance and noise in patch tokens would damage the performance of ViT-based models. In this paper, we develop Sufficient Vision Transformer (Suf-ViT) as a new solution to address this issue. In our research, we propose the Sufficiency-Blocks (S-Blocks) to be applied across the depth of Suf-ViT to disentangle and discard task-irrelevant information accurately. Besides, to boost the training of Suf-ViT, we formulate a Sufficient-Reduction Loss (SRLoss) leveraging the concept of Mutual Information (MI) that enables Suf-ViT to extract more reliable sufficient representations by removing task-irrelevant information. Extensive experiments on benchmark datasets such as ImageNet, ImageNet-C, and CIFAR-10 indicate that our method can achieve state-of-the-art or competing performance over other baseline methods. Codes are available at: https://github.com/zhicheng2T0/Sufficient-Vision-Transformer.git
Data-driven Humanitarian Mapping and Policymaking

Human civilization faces existential threats in the forms of climate change, food insecurity, pandemics, international conflicts, forced displacements, and environmental injustice. These overarching humanitarian challenges disproportionately impact historically marginalized communities worldwide. UN OCHA estimates that 274 million people will need humanitarian support in 2022. Despite growing perils to human and environmental well-being, there remains a paucity of publicly-engaged computing research to inform the design of interventions. Data science efforts exist, but they remain isolated from socioeconomic, environmental, cultural, and policy contexts at local and international scales. Moreover, biases and privacy infringements in data-driven methods further amplify existing inequalities. The result is that proclaimed benefits of data-driven innovations may remain inaccessible to policymakers, practitioners, and underserved communities whose lives they intend to transform. To address gaps in knowledge and improve the livelihood of marginalized populations, we have established the Data-driven Humanitarian Mapping and Policymaking, an interdisciplinary initiative.
Global Correlation Enhanced Hand Action Recognition Based on NST-GCN

Hand action recognition is an important part of intelligent monitoring, human–computer interaction, robotics and other fields. Compared with other methods, the hand action recognition method using skeleton information can ignore the error effects caused by complex background and movement speed changes, and the computational cost is relatively small. The spatial-temporal graph convolution networks (ST-GCN) model has excellent performance in the field of skeleton-based action recognition. In order to solve the problem of the root joint and the further joint not being closely connected, resulting in a poor hand-action-recognition effect, this paper firstly uses the dilated convolution to replace the standard convolution in the temporal dimension. This is in order to process the time series features of the hand action video, which increases the receptive field in the temporal dimension and enhances the connection between features. Then, by adding non-physical connections, the connection between the joints of the fingertip and the root of the finger is established, and a new partition strategy is adopted to strengthen the hand correlation of each joint point information. This helps to improve the network’s ability to extract the spatial-temporal features of the hand. The improved model is tested on public datasets and real scenarios. The experimental results show that compared with the original model, the 14-category top-1 and 28-category top-1 evaluation indicators of the dataset have been improved by 4.82% and 6.96%. In the real scene, the recognition effect of the categories with large changes in hand movements is better, and the recognition results of the categories with similar trends of hand movements are poor, so there is still room for improvement.
Time-Efficient Algorithm for Data Annotation using Deep Learning

Current generation emphasis on the Digital world which creates a lot of unbeneficial data. The paper is about data annotation using deep learning as there is a lot of data available online but which data is useful can be labeled using these techniques. The unstructured data is labeled by many techniques but implementation of deep learning for labeling the unstructured data results in saving the time with high efficiency. In this paper introduce a method for data annotation, for that we can use unlabeled data as input and it is classified using the K-Nearest Neighbor algorithm. K-Nearest Neighbor is the fastest and its accuracy is very high compared to other classification algorithms. After classified the unlabeled data we use it as input and annotate data using deep learning techniques. In deep learning we use an auto annotator for annotating data. After annotating data, check the accuracy of annotated data and time efficiency of data annotation. In case the accuracy is low then we can retrain the data and make it more accurate.
On the Effectiveness of True Random Number Generators Implemented on FPGAs

Randomness is at the core of many cryptographic implementations. True random number generators provide unpredictable sequences of numbers by exploiting physical phenomena. This work compares multiple literature proposals of true random number generators targeting FPGAs. The considered TRNGs are obtained as the combinations of three digital noise sources, namely, NLFIRO, PLL-TRNG, and ES-TRNG, and three post-processing techniques, namely, XOR, Von Neumann, and LFSR. The resulting combinations of such components are evaluated in terms of security, throughput, and resource utilization. The experimental results, which were collected on Xilinx Artix-7 FPGAs, highlight the importance of the post-processing stage for security purposes and reveal NLFIRO as the best digital noise source and LFSR as the best post-processing technique, having the highest throughput with excellent security performance without compromising area and power consumption.
Absolute Moment Block Truncation Coding and Singular Value Decomposition-Based Image Compression Scheme Using Wavelet

The present paper proposes a new hybrid image compression method via the incorporation of the methods, namely Singular Values Decomposition (SVD), Discrete Wavelet Transform (DWT), and Absolute Moment Block Truncation Coding (AMBTC). In order to maneuver these unified methods, DWT compression gives high compression, whereas SVD gives a low compression ratio but gives high image quality. In our new method, the image has firstly compressed with SVD and then further compressed by DWT and AMBTC. The image compressed by SVD is the input image for DWT, and it breaks up into its approximate coefficients and detail coefficients. Further, only approximate image is coded through AMBTC and details images are discarded because of containing less significant data. The newly proposed image compression algorithm has been checked on different natural images, and the results have been compared with other standard methods like BTC, AMBTC, and DWT-AMBTC. The new technique gets a high compression ratio of 20:1 and bitrates to reach 0.4 bits per pixel with good image quality. The objective and subjective results of our method are better than the other compression techniques. The goal of this idea is to attain high compression, and the high compression ratio is received comparatively higher than all other methods with comparable PSNR values for all images.
Unethical practices in online classes during COVID-19 pandemic: an analysis of affordances using routine activity theory

Purpose While online classes have enabled many universities to carry out their regular academic activities, they have also given rise to new and unanticipated ethical concerns. We focus on the “dark side” of online class settings and attempt to illuminate the ethical problems associated with them. The purpose of this study is to investigate the affordances stemming from the technology-user interaction that can result in negative outcomes. We also attempt to understand the context in which these deleterious affordances are actualized. Design/methodology/approach We obtain the data from narratives written by students at a top private university in Bangladesh about their experiences of online classes and exams and from focus group discussions with them. We use the lens of affordance theory to identify the abilities that goal-oriented actors – primarily students – obtain from the technology-user interactions, which result in negative outcomes. We also attempt to understand the contextual actualization of those affordances through the lens of Routine Activity Theory (RAT). Findings We find three deleterious affordances and three associated deviant outcomes. Non-monitorability which results in academic dishonesty, disguiseability which results in cyber-truancy, and intrudeability which results in embarrassment and harassment. Our findings reveal a deeper underlying problem with the existing educational approach in the universities of Bangladesh and suggest that there is a need to introduce more modern teaching techniques focused on issues such as student engagement and interactive learning. Originality/value To the best of our knowledge, this is the first paper that combines affordance theory with RAT to identify unethical practices observed in online class settings in the context of a least developed country like Bangladesh and to examine the environmental components that give rise to the pre-conditions for the unethical practices to surface.
Service Desk Onboarding Training Environment

Low qualification of employees newly hired to service desks contributes to the high turnover of service desk agents and consequently to low quality of services delivered.This paper proposes a conceptual artefact comprising two modules for tacit knowledge elicitation and knowledge transfer during the onboarding training process.The design of the artefact follows the design science methodology.Exante evaluation methods are chosen to evaluate the importance of a problem domain and evaluate the artefact feasibility.Expert interviews and focus group discussions with experts from the field were performed to support the evaluation activities.The proposed framework uses eye-tracking technology to complement captured knowledge with tacit knowledge.Next, the proposed model incorporates a simulated environment for enhanced training experience and effective knowledge transfer from expert employees to novice ones.This paper and the proposed artefact aim to improve the training process of service desk employees and to contribute to wider use of tacit knowledge capture and elicitation techniques in IT service management.
An Intelligent Assessment Method of English Teaching Ability Based on Improved Machine Learning Algorithm

In order to fully exploit the effect of intelligent evaluation of English teaching ability and explore the quality of English teaching, an intelligent evaluation method of English teaching ability based on improved machine learning algorithm is proposed, which can ensure the rational allocation of English teaching resources, analyze the big data of constraint parameters of English teaching ability evaluation, and obtain frequent item sets of English teaching quality based on big data mining technology. The particle swarm optimization (PSO) method was used to improve the parameters of the SVM, and the optimal parameters of the support vector machines (SVM) were obtained, which were input into the sample of English teaching effect evaluation, and a method was constructed to maximize the SVM. The optimal parameters are introduced into the decision function, so as to achieve the purpose of English teaching quality assessment. The experimental results show that the proposed method has a high convergence speed and can achieve rapid convergence with only 30 network trainings. At the same time, the evaluation accuracy of English teaching quality is as high as 95%, the correlation coefficient of the results is as high as 0.95, and the evaluation time is low. It is less than 150 ms, and the keyword frequency identification is better and better, which can realize the objective evaluation of online teaching quality.
The task system for the first All-Russian Olympiad in artificial intelligence for schoolchildren

The article discusses the main approaches to the development and use of the task system for the first All-Russian Olympiad in artificial intelligence for schoolchildren. The first Olympiad in artificial intelligence for schoolchildren, held by the Ministry of Education of the Russian Federation, had distinctive features in the procedure of the competition and in competition tasks. These features can be used for comparative analysis and expert evaluation of intellectual competitions for schoolchildren held in Russia. The subjects of the Olympiad tasks are Natural Language Processing, Computer Vision, and Data Science. The Python programming language was used. The tasks were focused on the formation of an individual path for the student to prepare for the final stage of the Olympiad in artificial intelligence, i. e. thematic continuity was observed at all stages and the practical orientation of tasks using data sets was observed in the second and third rounds. For the assessment, a rating assessment was used (as in the professional community Kaggle.com) of the individual abilities of schoolchildren in the complex application of mathematical and digital skills in solving artificial intelligence tasks. The article may be useful for informatics teachers who include the topic in an advanced informatics course and teachers of additional education who prepare schoolchildren for Olympiads in programming.
Using Blockchains for Censorship-Resistant Bootstrapping in Anonymity Networks

With Tor being a popular anonymity network, many censors and ISPs have blocked access to it. Tor relies on privately and selectively distributing IPs of circumvention proxies (i.e., bridges) to censored clients for censorship evasion. However, existing distributors are still vulnerable to blocking or compromising anonymity. This paper introduces Antiblok, a new and practical channel for bridge distribution leveraging blockchain, a globally decentralized environment. A key insight of Antiblok is that all blockchain transactions are under pseudonymous identities, allowing requesting clients to fetch bridge information while maintaining anonymity, regardless of the trustworthiness of blockchain nodes. To prevent the use of off-chain communication channels, we present an account sharing protocol based on DH key exchange. The unblockability of Antiblok depends on the economic consequences of blocking the Ethereum system. We show that Antiblok effectively thwarts client-side blocking of the distribution channel for Tor bridges, and we describe the security of our design.
Scalable crash consistency for secure persistent memory

Persistent memory (PM) suffers from data security and crash consistency issues due to non-volatility. Counter-mode encryption (CME) and bonsai merkle tree (BMT) have been adopted to ensure data security by using security metadata. The data and its security metadata need to be atomically persisted for correct recovery. To ensure crash consistency, durable transactions have been widely employed. However, the long-time BMT update increases the transaction latency, and the security metadata incur heavy write traffic. This paper presents Secon to ensure SEcurity and crash CONsistency for PM with high performance. Secon leverages a scalable write-through metadata cache to ensure the atomicity of data and its security metadata. To reduce the transaction latency, Secon proposes a transaction-specific epoch persistency model to minimize the ordering constraints. To reduce the amount of PM writes, Secon co-locates counters with log entries and coalesces BMT blocks. Experimental results demonstrate that Secon significantly improves the transaction performance and decreases the write traffic.
IA-FPN: Interactive Aggregation Feature Pyramid Network for Action Detection

Action detection (Spatio-temporal action localization) is a task of locating the actors in every frame of the video and classifying their actions. The difficulty of action detection is how to interactively aggregate features to effectively extract spatio-temporal information. We propose the Interactive Aggregation Feature Pyramid Network (IA-FPN), which can well integrate 2D convolution features and 3D convolution features. It is divided into two steps: first aggregate 2D convolution features and 3D convolution features respectively, and then further aggregate the fused features at different resolutions. Besides, we improve the positive and negative sample allocation strategy based on yolov3, which can effectively improve the convergence speed of action detection network. The proposed algorithm's Frame-mAP can achieve 84.5% and 87.0% on benchmark datasets of J-HMDB-21 and UCF101-24 respectively, compared with the most state-of-the-art algorithms in recent years, our method achieves competitive performance.
ER-Net: Efficient Recalibration Network for Multi-view Multi-person 3D Pose Estimation

Multi-view multi-person 3D human pose estimation is a hot topic in the field of human pose estimation due to its wide range of application scenarios. With the introduction of end-to-end direct regression methods, the field has reached a new stage of development. However, the regression results are still not accurate enough even for the optimal method for the joints that are more heavily influenced by external factors. In this paper, we propose an effective feature recalibration module based on the channel attention mechanism and an optimal calibration strategy, which is applied to a multi-view multi-person 3D human pose estimation task backbone network to achieve improved detection accuracy for joints that are more severely affected by external factors. Specifically, we achieve optimal weight adjustment of joint feature information through a recalibration module based on a channel attention mechanism and an optimal calibration strategy, which enables the model to learn the dependencies between joints and the dependencies between people and their corresponding joints. We call this method the Efficient Recalibration Network (ER-Net). Finally, experiments were conducted on two benchmark datasets for this task, Campus, and Shelf, PCP reached 97.3 percent and 98.3 percent, respectively.