Reviewerly/exHarmony huggingface dataset:
Naukometriya, Nalimov and Mul’chenko

In this article, we try to find out what Vasiliy Nalimov and Zinaida Mul’chenko exactly wrote in their famous book Naukometriya, the Study of the Development of Science as an Information Process (1969). Afterward, the term Naukometriya became known in the West as Scientometrics.It is often mentioned that Nalimov invented the term scientometrics, usually with reference to his book published in 1969. Shouldn’t Mul’chenko be mentioned as one of the inventors of this term? We show that referring to Naukometriya is a case of wrong reference, as Nalimov introduced the term already in 1966.We further place the term scientometrics in the context of the science of science and of Soviet science during that period.
Privacy-preserving AI-enabled video surveillance for social distancing: responsible design and deployment for public spaces

Purpose The paper proposes a privacy-preserving artificial intelligence-enabled video surveillance technology to monitor social distancing in public spaces. Design/methodology/approach The paper proposes a new Responsible Artificial Intelligence Implementation Framework to guide the proposed solution's design and development. It defines responsible artificial intelligence criteria that the solution needs to meet and provides checklists to enforce the criteria throughout the process. To preserve data privacy, the proposed system incorporates a federated learning approach to allow computation performed on edge devices to limit sensitive and identifiable data movement and eliminate the dependency of cloud computing at a central server. Findings The proposed system is evaluated through a case study of monitoring social distancing at an airport. The results discuss how the system can fully address the case study's requirements in terms of its reliability, its usefulness when deployed to the airport's cameras, and its compliance with responsible artificial intelligence. Originality/value The paper makes three contributions. First, it proposes a real-time social distancing breach detection system on edge that extends from a combination of cutting-edge people detection and tracking algorithms to achieve robust performance. Second, it proposes a design approach to develop responsible artificial intelligence in video surveillance contexts. Third, it presents results and discussion from a comprehensive evaluation in the context of a case study at an airport to demonstrate the proposed system's robust performance and practical usefulness.
Radiation intensity Gaussian mixture PHD filter for close target tracking

The Gaussian mixture probability hypothesis density (GM-PHD) filter has recently been devised as a suboptimal Bayesian solution for multiple target tracking. However, the performance of standard GM-PHD degrades seriously in close targets tracking. Although the renormalization scheme is proposed to improve the estimation results, the identification problem of measurements generated by different targets and clutter is still unresolved. To this end, this paper develops a radiation intensity PHD filter (RIGM-PHD). First, the Gaussian distribution with signal-to-noise (SNR) information is proposed to model the radiation intensity and describe the relationship between the target radiation intensity and clutter level. Then, in order to circumvent the issue that the real target SNR can't be obtained precisely, we construct a likelihood function of radiation intensity for unknown target SNR and derive new PHD recursion equations. Second, a labeling update scheme is also provided to prevent the incorrect propagation of identities of close targets. Finally, comprehensive Monte Carlo simulations in terms of various detection probabilities and clutter rates for parallel and crossing targets are performed to investigate the effectiveness of the proposed filter.
Post-Processing of NWP Forecasts Using Kalman Filtering With Operational Constraints for Day-Ahead Solar Power Forecasting in Thailand

Solar power forecasting with a day-ahead horizon has played an important role in the operational planning of generating units in power system operations.We aim to develop a solar power forecasting model suitable for a tropical climate, using Thailand as a model, and hence present a linear recursive regression model as a post-processing step for reducing the errors obtained from the Weather Research and Forecasting (WRF) model.This model consists of submodels, each of which predicts the solar irradiance of a particular time of the day.By using a stepwise regression method, we found that WRF forecasts of irradiance, temperature, relative humidity, and the solar zenith angle were selected as highly relevant inputs of the model.The regression model coefficients are updated according to a Kalman filtering (KF) scheme so that the model can flexibly adapt to fluctuations in the solar irradiance.We then modify the KF update formula to accommodate the limitation in measurement availability at the time of executing the forecasts.The proposed KF formula can be generalized to find the optimal prediction given that the available measurements are mapped by an affine transformation.The obtained results using actual data from a solar rooftop system located in the central region of Thailand showed that the normalized rootmean-square error (NRMSE) of solar power prediction was about 12-13%, which was decreased from the NRMSE of the WRF model by 7-12% on average.This improvement was the best out of similar post-processing methods based on the model output statistics framework.
Cheating Detection Pipeline for Online Interviews

Global precautions against the pandemic made the online meeting systems widespread. Most of the companies and academic institutions utilize these systems for their recruitment processes and also for online exams. This led to the integration of anti-cheating analysis becoming a necessity for online meetings. We built an ideal pipeline for such an anti-cheating system and designed its components. These components may vary depending on use cases. However, some basic functionalities must remain for proper software. The pipeline consists of Face detection, Face recognition, Object detection, Face tracking, and Result analysis. We evaluated this pipeline and its components on a private interview video dataset. The dataset is labeled by its cheating status for overall video and also by the presence of individual cheating events. We utilized faster implementations of squeezed versions of up-to-date deep learning detection models to be able to process videos faster. Ultimately, our pipeline presents a guideline to detect and analyze cheating activities in an online interview video efficiently.
Leveraging implicit relations for recommender systems

Collaborative filtering (CF) is one of the dominant techniques used in recommender systems. Most CF-based methods treat every user (or item) as an isolated existence, without explicitly modeling potential mutual relations among users (or items), which are latent in user-item interactions. In this paper, we design a novel strategy to mine user-user and item-item implicit relations and propose a natural way of utilizing the implicit relations for recommendation. Specifically, our method contains two major phases: neighbor construction and recommendation framework. The first phase constructs an implicit neighbor set for each user and item according to historical user-item interaction. In the second phase, based on the constructed neighbor sets, we propose a deep framework to generate recommendations. We conduct extensive experiments with four datasets on the movie, business, book, and restaurant recommendations and compare our methods with seven baselines, e.g., feature-based, neighborhood-based, and graph-based models. The experiment results demonstrate that our method achieves superior performance in rating prediction and top-k recommendation.
A Multi‐Granularity Density Peak Clustering Algorithm Based on Variational Mode Decomposition

Density peak clustering (DPC) can identify cluster centers quickly, without any prior knowledge. It is supposed that the cluster centers have a high density and large distance. However, some real datasets have a hierarchical structure, which will result in local cluster centers having a high density but a smaller distance. DPC is a flat clustering algorithm that searches for cluster centers globally, without considering local differences. To address this issue, a Multi-granularity DPC (MG-DPC) algorithm based on Variational mode decomposition (VMD) is proposed. MG-DPC can find global cluster centers in the coarse-grained space, as well as local cluster centers in the fine-grained space. In addition, the density is difficult to calculate when the dataset has a high dimension. Neighborhood preserving embedding (NPE) algorithm can maintain the neighborhood relationship between samples while reducing the dimensionality. Moreover, DPC requires human experience in selecting cluster centers. This paper proposes a method for automatically selecting cluster centers based on Chebyshev’s inequality. MG-DPC is implemented on the dataset of load-data to realize load classification. The clustering performance is evaluated using five validity indices compared with four typical clustering methods. The experimental results demonstrate that MG-DPC outperforms other comparison methods.
A Hierarchical Coordination Framework for Joint Perception-Action Tasks in Composite Robot Teams

We propose a collaborative planning and control algorithm to enhance cooperation for composite teams of autonomous robots in dynamic environments. Composite robot teams are groups of agents that perform different tasks according to their respective capabilities in order to accomplish an overarching mission. Examples of such teams include groups of perception agents (can only sense) and action agents (can only manipulate) working together to perform disaster response tasks. Coordinating robots in a composite team is a challenging problem due to the heterogeneity in the robots’ characteristics and their tasks. Here, we propose a coordination framework for composite robot teams. The proposed framework consists of two hierarchical modules: First, A multiagent state-action-reward-time-state-action algorithm in multiagent partially observable semi-Markov decision process as the high-level decision-making module to enable perception agents to learn to surveil in an environment with an unknown number of dynamic targets and second, a low-level coordinated control and planning module that ensures probabilistically guaranteed support for action agents. Simulation and physical robot implementations of our algorithms on a multiagent robot testbed demonstrated the efficacy and feasibility of our coordination framework by reducing the overall operation times in a benchmark wildfire-fighting case study.
Unbalanced abnormal traffic detection based on improved Res-BIGRU and integrated dynamic ELM optimization

Problems such as a vanishing gradient and overfitting will occur when a recurrent neural network (RNN) is exploited to detect abnormal network traffic. In addition, some network traffic is unbalanced, which leads to low detection accuracy. Therefore, an unbalanced abnormal traffic detection method has been proposed. It is composed of the improved bidirectional residual gated recurrent unit (Res-BIGRU) and integrated dynamic extreme learning machine (IDELM). First, the candidate hidden state activation function of the GRU is changed into an unsaturated activation function. The residual connection is used to avoid the vanishing gradient. The purpose of alleviating network degradation is achieved, and the traffic features extracted are better. Second, an IDELM is proposed to solve the unbalanced classification. The minority samples are generated by the IDELM model. The set model in game theory is used to compute the combined weight, which improves the fitting effect. Third, two IDELMs are used to update the final classification results. Fourth, four network datasets and IoT datasets are used to verify the performance. The average accuracy on four network datasets is 91.11% when samples are unbalanced. Furthermore, it can be concluded that the improved Res-BIGRU and IDELM strategy is effective. Better classification results can be achieved when network traffic is unbalanced. In particular, the performance is better in unbalanced NSL-KDD datasets. The index values obtained are the best compared with other methods. It is also suitable for intrusion detection of the Internet of Things, which has good performance. The further advantage lies in that the robustness is better when there are other sample interferences.
ASMR: Learning Attribute-Based Person Search with Adaptive Semantic Margin Regularizer

Attribute-based person search is the task of finding person images that are best matched with a set of text attributes given as query. The main challenge of this task is the large modality gap between attributes and images. To reduce the gap, we present a new loss for learning cross-modal embeddings in the context of attribute-based person search. We regard a set of attributes as a category of people sharing the same traits. In a joint embedding space of the two modalities, our loss pulls images close to their person categories for modality alignment. More importantly, it pushes apart a pair of person categories by a margin determined adaptively by their semantic distance, where the distance metric is learned end-to-end so that the loss considers importance of each attribute when relating person categories. Our loss guided by the adaptive semantic margin leads to more discriminative and semantically well-arranged distributions of person images. As a consequence, it enables a simple embedding model to achieve state-of-the-art records on public benchmarks without bells and whistles.
Memory-based Semantic Segmentation for Off-road Unstructured Natural Environments

With the availability of many datasets tailored for autonomous driving in real-world urban scenes, semantic segmentation for urban driving scenes achieves significant progress. However, semantic segmentation for off-road, unstructured environments is not widely studied. Directly applying existing segmentation networks often results in performance degradation as they cannot overcome intrinsic problems in such environments, such as illumination changes. In this paper, a built-in memory module for semantic segmentation is proposed to overcome these problems. The memory module stores significant representations of training images as memory items. In addition to the encoder embedding like items together, the proposed memory module is specifically designed to cluster together instances of the same class even when there are significant variances in embedded features. Therefore, it makes segmentation networks better deal with unexpected illumination changes. A triplet loss is used in training to minimize redundancy in storing discriminative representations of the memory module. The proposed memory module is general so that it can be adopted in a variety of networks. We conduct experiments on the Robot Unstructured Ground Driving (RUGD) dataset and RELLIS dataset, which are collected from off-road, unstructured natural environments. Experimental results show that the proposed memory module improves the performance of existing segmentation networks and contributes to capturing unclear objects over various off-road, unstructured natural scenes with equivalent computational cost and network parameters. As the proposed method can be integrated into compact networks, it presents a viable approach for resource-limited small autonomous platforms.
Activity guided multi-scales collaboration based on scaled-CNN for saliency prediction

Visual saliency prediction has achieved significant improvements with the advent of convolutional neural networks, but the breakthrough in saliency prediction accuracy comes at the high computational cost. In this paper, we present a lightweight saliency prediction model based on scaled up convolutional neural networks (CNN), utilizing image activity guided collaboration learning of global and local information at multiple scales. we use a pseudo-siamese network with a scaled up network (EfficientNet) as the backbone, and the two branches of the network respectively capture the global saliency feature and high-level local feature. Concretely, we first utilize the image complexity-related activity features (Image Activity Measure) as our low-level local salience prior, and then feed the input images and the activity maps to scaled up CNN modules to further learn high-level features in a multi-scale collaboration manner. Through extensive evaluation, we show that the proposed method exhibits competitive and consistent results on the challenging benchmark datasets, and our method has better prediction performance, fewer trainable parameters and faster inference speed. Moreover, the proposed model has low requirements for platform computing capabilities, which improves the universality of saliency application scenarios.
Road Garbage Segmentation and Cleanliness Assessment Based on Semantic Segmentation Network for Cleaning Vehicles

Road garbage recognition and cleanliness assessment are very important for intelligent cleaning of vehicles. However, widely used garbage detection methods cannot provide accurate information to assess cleanliness. Also, only a few contributions are available in the road cleanliness assessment, and they too mainly depend on the category and quantity of garbage. In this paper, deep supervision UNet++ (DUNet++) is proposed to solve the problem of road garbage classification and segmentation, which can directly impact the output of the category of garbage and the occupied ground area. A new, simple and accurate method is established to assess road cleanliness by combining the results of garbage segmentation. Compared with the cleanliness index (CI), the road cleanliness index based on semantic segmentation (CISS) not only considers the road area occupied by different types or categories of the garbage but also considers the difficulty of cleaning them. A road garbage segmentation dataset, which consists of four categories (stones, leaves, sand, and bottles), is collected to train the designed garbage segmentation model, and it has achieved considerable garbage segmentation improvement with an MIoU (Mean Intersection over Union) of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$76.73\pm 0.11$</tex-math></inline-formula> . Especially when compared with the state-of-the-art methods, the model we designed has greatly improved the accuracy of garbage segmentation. Experimental results have shown that our road garbage segmentation and cleanliness assessment methods are relatively simple, and can provide accurate road cleanliness information for cleaning vehicles.
Secure Private and Adaptive Matrix Multiplication Beyond the Singleton Bound

We consider the problem of designing secure and private codes for distributed matrix-matrix multiplication. A master server owns two private matrices and hires worker nodes to help compute their product. The matrices should remain information-theoretically private from the workers. Some of the workers are malicious and return corrupted results to the master. We design a framework for security against malicious workers in private matrix-matrix multiplication. The main idea is a careful use of Freivalds’ algorithm to detect erroneous matrix multiplications. Our main goal is to apply this security framework to schemes with adaptive rates. Adaptive schemes divide the workers into clusters and thus provide flexibility in trading decoding complexity for efficiency. Our new scheme, SRPM3, provides a computationally efficient security check per cluster that detects the presence of one or more malicious workers with high probability. An additional per worker check is used to identify the malicious nodes. SRPM3 can tolerate the presence of an arbitrary number of malicious workers. We provide theoretical guarantees on the complexity of the security checks and simulation results on both, the missed detection rate as well as on the time needed for the integrity check.
HNF: Hybrid Neural Filtering Based on Centrality-Aware Random Walk for Personalized Recommendation

Social computing which analyzes users’ behaviors can help personalized recommender system to extract preferences of users. Most of personalized recommender systems exploit a user-item rating matrix to learn representations of users and items for predicting users’ ratings on items. In this paper, we design a new framework, called HNF, to learn two kinds of representations and fuse them for recommendation. Our HNF consists of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">topological neural filtering</i> (TNF) module, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">collaborative neural filtering</i> (CNF) module and prediction module. The TNF module is to learn topological representations of user-item interactions from a user-item bipartite graph constructed based on a user-item rating matrix. The CNF module is to learn collaborative representations of user-item interactions. The prediction module aims to fuse the topological representations and collaborative representations to generate hybrid representations for rating prediction. We conduct experiments on three real-world public datasets. Results validate that our proposed HNF algorithm outperforms the state-of-the-art algorithms in terms of higher evaluation metrics.
Formal security analysis of MPC-in-the-head zero-knowledge protocols

Zero-knowledge proofs allow a prover to convince a verifier of the veracity of a statement without revealing any other information. An interesting class of zero-knowledge protocols are those following the MPC-in-the-head paradigm (Ishai et al., STOC '07) which use secure multiparty computation (MPC) protocols as the basis. Efficient instances of this paradigm have emerged as an active research topic in the last years, starting with ZKBoo (Giacomelli et al., USENIX '16). Zero-knowledge protocols are a vital building block in the design of privacy-preserving technologies as well as cryptographic primitives like digital signature schemes that provide post-quantum security. This work investigates the security of zero-knowledge protocols following the MPC-in-the-head paradigm. We provide the first machine-checked security proof of such a protocol on the example of ZKBoo. Our proofs are checked in the EasyCrypt proof assistant. To enable a modular security proof, we develop a new security notion for the MPC protocols used in MPC-in-the-head zero-knowledge protocols. This allows us to recast existing security proofs in a black-box fashion which we believe to be of independent interest.
OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution

Images tell powerful stories but cannot always be trusted. Matching images back to trusted sources (attribution) enables users to make a more informed judgment of the images they encounter online. We propose a robust image hashing algorithm to perform such matching. Our hash is sensitive to manipulation of subtle, salient visual details that can substantially change the story told by an image. Yet the hash is invariant to benign transformations (changes in quality, codecs, sizes, shapes, etc.) experienced by images during online redistribution. Our key contribution is OSCAR-Net <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> (Object-centric Scene Graph Attention for Image Attribution Network); a robust image hashing model inspired by recent successes of Transformers in the visual domain. OSCAR-Net constructs a scene graph representation that attends to fine-grained changes of every object’s visual appearance and their spatial relationships. The network is trained via contrastive learning on a dataset of original and manipulated images yielding a state of the art image hash for content fingerprinting that scales to millions of images.