Reviewerly/exHarmony huggingface dataset:
Optical-flow-based framework to boost video object detection performance with object enhancement

With image object detection techniques progressing, video object detection attracts a lot of attention more than ever before. However, the performance of detecting the object from a video suffers a lot as object occlusion can always lead to the object in the presence of appearance deterioration. To deal with this problem, the temporal correlation of video sequences is often used to reduce the effects of object occlusion in video object detection. In this paper, an optical-flow-feature fusion-based video object detection method is proposed with consideration of temporal coherence among video frames. To further reduce computational complexity, this paper also proposes a packet video processing method. Specifically, video frames are grouped first, and all frames in current group share the same optical flow feature map by feature fusion. Then the Target Image can be formed with object information enhanced by fusing the shared feature map with current frame. The proposed method gives rise to effective background information masking, so that the object detection network can focus more on the foreground object. This method effectively improves the object detection performance and the scene migration performance. Experimental results prove that the proposed method significantly improves the detection accuracy to 78.6% on ImageNet VID. In addition, VGG and ResNet are compared to further verify the effectiveness of the proposed method, and the results can be a persuasive evidence with the highest detection accuracy and acceptable time consumption.
Online Trajectory Planning for Multiple Quadrotors in Dynamic Environments Using Relative Safe Flight Corridor

This letter presents a new distributed multi-agent trajectory planning algorithm that generates safe, dynamically feasible trajectories considering the uncertainty of obstacles in dynamic environments. We extend the relative safe flight corridor (RSFC) presented in previous work to replace time-variant, non-convex collision avoidance constraints to convex ones, and we adopt a relaxation method based on reciprocal collision avoidance (RCA) to reduce the total flight time and distance without loss of success rate. The proposed algorithm can compute the trajectories for 50 agents on average 49.7 ms per agent with Intel i7 desktop and can generate safe trajectories for 16 agents with a success rate of more than 93% in simulation environments with four dynamic obstacles when the velocity of dynamic obstacles is below the maximum velocity of quadrotors. We validate the robustness of the proposed algorithm through a real flight test with four quadrotors and one moving human.
A ranking-based feature selection for multi-label classification with fuzzy relative discernibility

Feature selection is a crucial pre-processing step for learning tasks to mitigate the “curse of dimensionality”, which is caused by irrelevant and redundant features in high-dimensional feature space. The fuzzy rough set is an effective tool in feature selection, and the fuzzy discernibility matrix, one of the generalized model of the fuzzy rough set, has attracted significant attention recently. However, with the complicated discrimination relation of multiple labels, the traditional fuzzy discernibility matrix does not fit well for multi-label data. To address this problem, in this paper, the fuzzy label discernibility relation and the fuzzy relative discernibility relation are defined firstly, which are derived from fuzzy discernibility relation. Subsequently, we present the feature discernibility significance which measures the discernibility ability of the conditional feature and selects the most relevant features using the value of discernibility significance. On this basis, we propose a ranking-based feature selection algorithm for multi-label classification with fuzzy relative discernibility. Finally, in terms of six widely-accepted multi-label evaluation metrics, a series of experiments is conducted based on the multi-label classifier with ten different multi-label datasets from Mulan library and MLL resource, to compare the performance of the proposed algorithm with four state-of-the-art multi-label feature selection algorithms. The experimental results demonstrate the superiority and effectiveness of the proposed algorithm in multi-label classification.
Straggler-Resilient Federated Learning: Leveraging the Interplay Between Statistical Accuracy and System Heterogeneity

Federated learning is a novel paradigm that involves learning from data samples distributed across a large network of clients while the data remains local. It is, however, known that federated learning is prone to multiple system challenges including system heterogeneity where clients have different computation and communication capabilities. Such heterogeneity in clients’ computation speed has a negative effect on the scalability of federated learning algorithms and causes significant slow-down in their runtime due to slow devices (stragglers). In this paper, we propose FLANP, a novel straggler-resilient federated learning meta-algorithm that incorporates statistical characteristics of the clients’ data to adaptively select the clients in order to speed up the learning procedure. The key idea of FLANP is to start the training procedure with faster nodes and gradually involve the slower ones in the model training once the statistical accuracy of the current participating nodes’ data is reached, while the final model for each stage is used as a warm-start model for the next stage. Our theoretical results characterize the speedup provided by the meta-algorithm FLANP in comparison to standard federated benchmarks for strongly convex losses and i.i.d. samples. For particular instances, FLANP slashes the overall expected runtime by a factor of O(ln(Ns)), where N and s denote the total number of nodes and the number of samples per node, respectively. In experiments, FLANP demonstrates significant speedups in wall-clock time –up to 6×– compared to standard federated learning benchmarks.
Round Optimal Secure Multiparty Computation from Minimal Assumptions

We construct a four round secure multip arty computation (MPC) protocol in the plain model that achieves security against any dishonest majority. The security of our protocol relies only on the existence of four round oblivious transfer. This culminates the long line of research on constructing round-efficient MPC from minimal assumptions (at least w.r.t. black-box simulation).
Part-of-speech tagging of building codes empowered by deep learning and transformational rules

Automated building code compliance checking systems were under development for many years. However, the excessive amount of human inputs needed to convert building codes from natural language to computer understandable formats severely limited their range of applicable code requirements. To address that, automated code compliance checking systems need to enable an automated regulatory rule conversion. Accurate Part-of-Speech (POS) tagging of building code texts is crucial to this conversion. Previous experiments showed that the state-of-the-art generic POS taggers do not perform well on building codes. In view of that, the authors are proposing a new POS tagger tailored to building codes. It utilizes deep learning neural network model and error-driven transformational rules. The neural network model contains a pre-trained model and one or more trainable neural layers. The pre-trained model was fine-tuned on Part-of-Speech Tagged Building Codes (PTBC), a POS tagged building codes dataset. The fine-tuning of pre-trained model allows the proposed POS tagger to reach high precision with a small amount of available training data. Error-driven transformational rules were used to boost performance further by fixing errors made by the neural network model in the tagged building code. Through experimental testing, the authors found a well-performing POS tagger for building codes that had one bi-directional LSTM trainable layer, utilized BERT_Cased_Base pre-trained model and was trained 50 epochs. This model reached a 91.89% precision without error-driven transformational rules and a 95.11% precision with error-driven transformational rules, which outperformed the 89.82% precision achieved by the state-of-the-art POS taggers.
Context-Gated Convolution

As the basic building block of Convolutional Neural Networks (CNNs), the convolutional layer is designed to extract local patterns and lacks the ability to model global context in its nature. Many efforts have been recently devoted to complementing CNNs with the global modeling ability, especially by a family of works on global feature interaction. In these works, the global context information is incorporated into local features before they are fed into convolutional layers. However, research on neuroscience reveals that the neurons’ ability of modifying their functions dynamically according to context is essential for the perceptual tasks, which has been overlooked in most of CNNs. Motivated by this, we propose one novel Context-Gated Convolution (CGC) to explicitly modify the weights of convolutional layers adaptively under the guidance of global context. As such, being aware of the global context, the modulated convolution kernel of our proposed CGC can better extract representative local patterns and compose discriminative features. Moreover, our proposed CGC is lightweight and applicable with modern CNN architectures, and consistently improves the performance of CNNs according to extensive experiments on image classification, action recognition, and machine translation. Our code of this paper is available at https://github.com/XudongLinthu/context-gated-convolution .
One-Way Functions Imply Secure Computation in a Quantum World

We prove that quantum-hard one-way functions imply simulation-secure quantum oblivious transfer (QOT), which is known to suffice for secure computation of arbitrary quantum functionalities. Furthermore, our construction only makes black-box use of the quantum-hard one-way function. Our primary technical contribution is a construction of extractable and equivocal quantum bit commitments based on the black-box use of quantum-hard one-way functions in the standard model. Instantiating the Crépeau-Kilian (FOCS 1988) framework with these commitments yields simulation-secure QOT.
DAN-SNR

Next (or successive) point-of-interest (POI) recommendation has attracted increasing attention in recent years. Most of the previous studies attempted to incorporate the spatiotemporal information and sequential patterns of user check-ins into recommendation models to predict the target user's next move. However, none of these approaches utilized the social influence of each user's friends. In this study, we discuss a new topic of next POI recommendation and present a deep attentive network for social-aware next POI recommendation called DAN-SNR. In particular, the DAN-SNR makes use of the self-attention mechanism instead of the architecture of recurrent neural networks to model sequential influence and social influence in a unified manner. Moreover, we design and implement two parallel channels to capture short-term user preference and long-term user preference as well as social influence, respectively. By leveraging multi-head self-attention, the DAN-SNR can model long-range dependencies between any two historical check-ins efficiently and weigh their contributions to the next destination adaptively. Also, we carried out a comprehensive evaluation using large-scale real-world datasets collected from two popular location-based social networks, namely Gowalla and Brightkite. Experimental results indicate that the DAN-SNR outperforms seven competitive baseline approaches regarding recommendation performance and is of high efficiency among six neural-network- and attention-based methods.
Experimental Investigation of Optoelectronic Receiver With Reservoir Computing in Short Reach Optical Fiber Communications

The cloud edge data center will enable reliable and low latency options for the network, and the interconnection among these data-centers will demand a scalable low-complexity scheme. An intensity-modulated and directed detected transmission system is an attractive solution, but chromatic dispersion is the main limitation for higher symbol rate systems. To overcome this challenge, we have proposed and experimentally demonstrated a receiver with shared-complexity between optical and digital domains that enables 80 km transmission reach below KP4 FEC limit for a 32 GBd on-off keying signal. The optical stage consists of optical filters that slices the signal into smaller sub-bands and each is detected by a photodetector. A feedforward neural network and reservoir computing are compared to reconstruct the full signal from the slices and mitigate the chromatic dispersion. Both equalizers have shown similar performance with the advantage of the reservoir computing requiring fewer inputs and easier training process. In this work, we have compared the linear and nonlinear activation functions in the feedforward neural network to investigate the gain of using a nonlinear equalizer. The maximum transmission reach is reduced almost to half, ≈45 km, when using the linear. The performance is also reduced if a reduced number of slices is used in the receiver, as we have demonstrated. In this case, using 2 slices to reduce the complexity of the system, instead of the total 4, we have shown a ≈55 km transmission reach below KP4 FEC limit. In this work we have also provided a numerical comparison with 4x8 GBd subcarriers system. The results have shown a 40 km increase in transmission reach compared to the proposed optoelectronic system. The trade-off between performance and complexity should be analyzed for each case, as a different hardware is required in each situation.
Conceptualising touch in VR

Abstract How touch is conceptualised matters in shaping technical advancements, bringing opportunities and challenges for development and design and raising questions for how touch experience is reconfigured. This paper explores the notion of touch in virtual reality (VR). Specifically, it identifies how touch ‘connection’ is realised and conceptualised in virtual spaces in order to explore how digital remediation of touch in VR shapes the sociality of touch experiences and touch practices. Ten participants from industry and academia with an interest in touch in virtual contexts were interviewed using an in-depth semi-structured approach to elicit experiences and perspectives around the role of touch in VR. Data analysis shows the growing value and significance of touch in virtual spaces and reveals particular ways in which touch is talked about, implemented and conceptualised. It highlights changes for the sociality of touch through participants’ conceptualisations of touch as replication and illusion, and how the body is brought into this ‘touch’ space. These perspectives of touch shape who touches, what is touched and how it is touched and set an agenda for the types of touch that are facilitated by VR. The findings suggest ways in which technological techniques can be employed towards interpretive designs of touch that allow for new ways to look at touch and haptics. They also show how touch is distorted and disrupted in ways that have implications for disturbing established ‘real world’ socialities of touch as well as their renegotiation by users in the space of digitally mediated touch in VR.
Geochemical and health risk assessment of heavy metals concentration in soils around Oke-Ere mining area in Kogi State, Nigeria

This study examined the levels of some heavy metals in soils of Oke-Ere mining area in Yagba West, Kogi State, Nigeria, aim at ascertaining the degree of pollution, and risk pose to the environment, and human health to the residents in the area. A total of 48 soil samples were analysed by using Inductively Coupled Plasma Mass Spectroscopy (ICP-MS). The mean concentrations (mg/kg) were As(1.92), Cd(< 0.004), Co(29.8), Cr(117), Mo(4.00), Ni(51.4), Pb(24.1), Sc(17.8), Sn(21.9), and Zn(85.8). Principal component analysis revealed significant anthropogenic contributions of Sc, Cr, Ni, and As in soils. Geochemical assessment evaluated based on models of geochemical load index (Igeo), enrichment factor (EF), contamination factor (Cf) and pollution load index (PLI), revealed that the soils were highly polluted by Sc, moderately polluted by Sn and non-polluted to slightly polluted by other metals. Enrichment factor shows significant enrichment by Sc, while Cr, Pb, Co, Mo and Sn were moderately enriched. The contamination factor indicated low contamination for most of the studied metals except Cu and Co which recorded considerable contamination. The values of pollution load index PLI > 1, signifies that the mining activities deteriorated soil quality. The total hazard index (HI) of the metals for adult and children, indicates HI < 1 which connotes a non-carcinogenic health risk effect. The cancer risk (CR) value for ingestion and dermal exposure pathways was higher than the permissible limits (1 x 10−6 to 1 × 10−4), which indicated adverse health risk to the local resident. This study suggested adequate attention be directed to detailed risk assessment of heavy metals in the study area.
Inception recurrent convolutional neural network for object recognition

Deep convolutional neural network (DCNN) is an influential tool for solving various problems in machine learning and computer vision. Recurrent connectivity is a very important component of visual information processing within the human brain. The idea of recurrent connectivity is rarely applied within convolutional layers, the exceptions being a couple of DCNN architectures including recurrent convolutional neural network (RCNN) in Liang and Hu (in: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015) and Pinheiro and Collobert (in: ICML, 2014). On the other hand, the Inception network architecture has become popular among the computer vision community (Szegedy et al. in Inception-v4, Inception-ResNet and the impact of Residual connections on learning, 2016. arXiv:1602.07261 ). In this paper, we introduce a deep learning architecture called the Inception Recurrent Convolutional Neural Network (IRCNN), which utilizes the power of an Inception network combined with recurrent convolutional layers. Although the inputs are static, the recurrent property plays a huge role in modeling the contextual information for object recognition tasks and thus improves overall training and testing accuracy. In addition, this proposed architecture generalizes both Inception and RCNN models. We have empirically evaluated the recognition performance of the proposed IRCNN model using different benchmark datasets such as MNIST, CIFAR-10, CIFAR-100, and SVHN. The experimental results show higher recognition accuracy when compared to most of the popular DCNNs including the RCNN. Furthermore, we have investigated IRCNN performance against equivalent Inception networks (EIN) and equivalent Inception–Residual networks (EIRN) using the CIFAR-100 dataset. When using the augmented CIFAR-100 dataset, we achieved about 3.5%, 3.47% and 2.54% improvement in classification accuracy compared to the RCNN, EIN, and EIRN respectively. We have also conducted experiment on Tiny ImageNet-200 dataset with IRCNN, EIN, EIRN, RCNN, DenseNet in Huang et al. (Densely connected convolutional networks, 2016. arXiv:1608.06993 ), and DenseNet with Recurrent Convolution Layer, where the proposed model shows significantly better performance against baseline models.
How Does Gender Balance In Training Data Affect Face Recognition Accuracy?

Deep learning methods have greatly increased the accuracy of face recognition, but an old problem still persists: accuracy is usually higher for men than women. It is often speculated that lower accuracy for women is caused by under-representation in the training data. This work investigates female under-representation in the training data is truly the cause of lower accuracy for females on test data. Using a state-of-the-art deep CNN, three different loss functions, and two training datasets, we train each on seven subsets with different male/female ratios, totaling forty two trainings, that are tested on three different datasets. Results show that (1) gender balance in the training data does not translate into gender balance in the test accuracy, (2) the “gender gap” in test accuracy is not minimized by a gender-balanced training set, but by a training set with more male images than female images, and (3) training to minimize the accuracy gap does not result in highest female, male or average accuracy.
The genesis, design and implications of China’s central bank digital currency

The People’s Bank of China (PBC) was among the first in the world to start exploration of central bank digital currency (CBDC) and will probably be one of the first major central banks releasing its own CBDC. This article intends to explain the construction of the e-CNY and discuss its likely implications. While PBC tried hard to avoid causing disintermediation of commercial banks, it remains to be seen if there will be a shift from commercial banks savings accounts to the new e-CNY wallets. E-CNY’s impacts on existing mobile payment system and the associated collection and analyses of big data in the Fintech sector could be major. In summary, even the modest step of creating e-CNY could significantly transform the financial landscape in China. But this is only the step by PBC in creating its own CBDC.
Connected Research: The Potential of the PID Graph

<h2>Summary</h2> Persistent identifiers (PIDs) provide unique and long-lasting references to entities. They enable unique identification persistently over time and hence play a crucial role in supporting the FAIR (Findable, Accessible, Interoperable, Reusable) principles. In this paper, we describe how the benefits of PIDs can be amplified by connecting them via their metadata. We are introducing the next step in PID infrastructure: the PID Graph. The PID Graph establishes connections between different entities within the research landscape, thereby enabling both researchers and institutions to access new information. The paper closes with three recommendations, which will help to optimize the use and value of PIDs within the research ecosystem.
Agile Project Development Issues During COVID-19

Today's software development business is very much dynamic, and industries are changing their software development and requirements ways to meet the new changes in the environment. The environment also demands the rapid development and delivery of the product. In fast-changing environment and demands, agile methodology is the most useful development method. It gains fame due to its unique features that facilitate the software development more efficiently, client developer relation, rapid delivery of the product, and allow changes at any stage of the project, and client satisfaction. Currently, the world is suffering under a pandemic situation due to the COVID-19 disease. The disease spread with the close interaction with the human, for this reason many thousands of developers started working from home. The software industries working with the agile methodology are facing many issues to meet the development objectives. For this line of research, we have conducted this study on many different software industries using the agile methodology. In this study, we interviewed many developers using the questionnaire to determine the significant reasons for the failure of agile methodology during the current pandemic situation. We applied regression analysis, Cronbach alpha, and descriptive analysis statistical methods to the data set.