Reviewerly/exHarmony huggingface dataset:
Adaptive Learning for Weakly Labeled Streams

In plenty of real-world applications, data are collected in a streaming fashion, and their accurate labels are hard to obtain. For instance, in the environmental monitoring task, sensors are collecting the data all the time. Still, their labels are scarce because the labeling process requires human effort and can conceal annotation errors. This paper investigates the problem of learning with weakly labeled data streams, in which data are continuously collected, and only a limited subset of streaming data is labeled but potentially with noise. This setting is challenging and of great importance but rarely studied in the literature. When the data are constantly gathered with unknown noise on labels, it is quite challenging to design algorithms to obtain a well-generalized classifier. To address this difficulty, we propose a novel noise transition matrix estimation approach for data streams with scarce noisy labels by online anchor points identification. Based on that, we propose an adaptive learning algorithm for weakly labeled data streams via model reuse and effectively alleviate the negative influence of label noise with unlabeled data. Both theoretical analysis and extensive experiments justify and validate the effectiveness of the proposed approach.
Sample-Efficient Kernel Mean Estimator with Marginalized Corrupted Data

Estimating the kernel mean in a reproducing kernel Hilbert space is central to many kernel-based learning algorithms. Given a finite sample, an empirical average is used as a standard estimation of the target kernel mean. Prior works have shown that better estimators can be constructed by shrinkage methods. In this work, we propose to corrupt data examples with noise from known distributions and present a new kernel mean estimator, called the marginalized kernel mean estimator, which estimates kernel mean under the corrupted distributions. Theoretically, we justify that the marginalized kernel mean estimator introduces implicit regularization in kernel mean estimation. Empirically, on a variety of tasks, we show that the marginalized kernel mean estimator is sample-efficient and obtains much lower estimation errors than the existing estimators.
Characterizing Covid Waves via Spatio-Temporal Decomposition

In this paper we develop a framework for analyzing patterns of a disease or pandemic such as Covid. Given a dataset which records information about the spread of a disease over a set of locations, we consider the problem of identifying both the disease's intrinsic waves (temporal patterns) and their respective spatial epicenters. To do so we introduce a new method of spatio-temporal decomposition which we call diffusion NMF (D-NMF). Building upon classic matrix factorization methods, D-NMF takes into consideration a spatial structuring of locations (features) in the data and supports the idea that locations which are spatially close are more likely to experience the same set of waves. To illustrate the use of D-NMF, we analyze Covid case data at various spatial granularities. Our results demonstrate that D-NMF is very useful in separating the waves of an epidemic and identifying a few centers for each wave.
The Good, the Bad, and the Outliers

Mathematical decision-optimization (DO) models provide decision support in a wide range of scenarios. Often, hard-to-model constraints and objectives are learned from data. Learning, however, can give rise to DO models that fail to capture the real system, leading to poor recommendations. We introduce an open-source framework designed for large-scale testing and solution quality analysis of DO model learning algorithms. Our framework produces multiple optimization problems at random, feeds them to the user's algorithm and collects its predicted optima. By comparing predictions against the ground truth, our framework delivers a comprehensive prediction profile of the algorithm. Thus, it provides a playground for researchers and data scientists to develop, test, and tune their DO model learning algorithms. Our contributions include: (1) an open-source testing framework implementation, (2) a novel way to generate DO ground truth, and (3) a first-of-its-kind, generic, cloud-distributed Ray and Rayvens architecture. We demonstrate the use of our testing framework on two open-source DO model learning algorithms.
RT-VeD

Real-time Vehicle-of-Interest (VoI) detection is becoming a core application to smart cities, especially in areas with high accident rates. With the increasing number of surveillance cameras and the advanced developments in edge computing, video tasks prefer to run on edge devices close to cameras due to the constraints of bandwidth, latency, and privacy concerns. However, resource-constrained edge devices are not competent for dynamic traffic loads with resource-intensive video analysis models. To address this challenge, we propose RT-VeD, a real-time VoI detection system based on the limited resources of edge nodes. RT-VeD utilizes multi-granularity computer vision models with different resource-accuracy trade-offs. It schedules vehicle tasks based on a traffic-aware actor-critic framework to maximize the accuracy of VoI detection while ensuring an inference time-bound. To evaluate the proposed RT-VeD, we conduct extensive experiments based on a real-world vehicle dataset. The experiment results demonstrate that our model outperforms other competitive methods.
pureGAM: Learning an Inherently Pure Additive Model

Including pairwise or higher-order interactions among predictors of a Generalized Additive Model (GAM) is gaining increasing attention in the literature. However, existing models face anidentifiability challenge. In this paper, we propose pureGAM, an inherently pure additive model of both main effects and higher-order interactions. By imposing thepureness condition to constrain each component function, pureGAM is proved to be identifiable without compromising accuracy. Furthermore, the pureness condition introduces additional interpretability in terms of simplicity. Practically, pureGAM is a unified model to support both numerical and categorical features with a novel learning procedure to achieve optimal performance. Evaluations show that pureGAM outperforms other GAMs and has very competitive performance even compared with opaque models, and its interpretability remarkably outperforms competitors in terms of pureness. We also share a successful adoption of pureGAM in one real-world application.
COSSUM: Towards Conversation-Oriented Structured Summarization for Automatic Medical Insurance Assessment

In medical insurance industry, a lot of human labor is required to collect information of claimants. Human assessors need to converse with claimants in order to record key information and organize it into a structured summary. With the purpose of helping save human labor, we propose the task of conversation-oriented structured summarization which aims to automatically produce the desired structured summary from a conversation automatically. One major challenge of the task is that the structured summary contains multiple fields of different types. To tackle this problem, we propose a unified approach COSSUM based on prompting to generate the values of all fields simultaneously. By learning all fields together, our approach can capture the inherent relationship between them. Moreover, we propose a specially designed curriculum learning strategy for model training. Both automatic and human evaluations are performed, and the results show the effectiveness of our proposed approach.
Research on Local Counting and Object Detection of Multiscale Crowds in Video Based on Time-Frequency Analysis

Objective. It has become a very difficult task for cameras to complete real-time crowd counting under congestion conditions. Methods. This paper proposes a DRC-ConvLSTM network, which combines a depth-aware model and depth-adaptive Gaussian kernel to extract the spatial-temporal features and depth-level matching of crowd depth space edge constraints in videos, and finally achieves satisfactory crowd density estimation results. The model is trained with weak supervision on a training set of point-labeled images. The design of the detector is to propose a deep adaptive perception network DRD-NET, which can better initialize the size and position of the head detection frame in the image with the help of density map and RGBD-adaptive perception network. Results. The results show that our method achieves the best performance in RGBD dense video crowd counting on five labeled sequence datasets; the MICC dataset, CrowdFlow dataset, FDST dataset, Mall dataset, and UCSD dataset were evaluated to verify its effectiveness. Conclusion. The experimental results show that the proposed DRD-NET model combined with DRC-ConvLSTM outperforms the existing video crowd counting ConvLSTM model, and the effectiveness of the parameters of each part of the model is further proved by ablation experiments.
Modeling of power consumption of the electrotechnical complex of the water supply system.

The article is devoted to solving the issue of energy consumption planning of complex energy technological complexes. The expediency of application of self-organization methods of models of complex systems for modeling of power consumption based on the principles of inductive modeling was substantiated. Power consumption modeling was performed using neural networks based on iterative algorithms of the group method of data handling. The use of GMDH neural networks provided automatic structural-parametric identification of the model. The search for the optimal structure of the model was performed in three classes of support functions of neural networks. As a result of the algorithm of synthesis of models, the set of models-candidates having optimum structure in its class was formed. The obtained models have high modeling quality and low forecast error. Complicating the structure of the neural network does not significantly improve the quality of modeling results. The selection of the best structure of the model was made from the standpoint of multicriteria selection using a morphometric approach. Previously, all the criteria were reduced to one type according to the function of the goal and the procedure of their normalization was performed. The best structure of the model was chosen taking into account the area of the figure formed by the normalized values of the selection criteria. The polynomial GMDH neural network of the first order was chosen as the power consumption model. Deviations of actual and model values of power consumption do not exceed 1%. The usage of GMDH neural networks as an algorithm for model synthesis, supplemented by multi-criteria selection of the best model structure, allowed making an unambiguous choice of the structure of the power consumption model, suitable for determining the energy baseline.
A Constructive Review on Pedestrian Action Detection, Recognition and Prediction

Analysis of pedestrian activities in the video sequences is an intriguing domain that incorporates vast applications, such as autonomous driving systems, traffic control systems and interactions between people and computers. The primary focus of this research was on evaluating several strategies to analyse pedestrian activities effectively. The constructive comparison included three main steps, i.e. detection of the pedestrian, recognition of their actions and prediction about the activity of the pedestrian. Changes in activities of pedestrians, dynamic background, moving camera, view angle and processing time made it more challenging. Recent approaches were justified and compared based on precision accuracy, processing time and minimum resource allocation. The results were also compared by a series of state-of-the-art research datasets with provided significant observations in terms of greater accuracy which can lead to the construction of an extremely improvised system that would save pedestrian people from road accidents and assist autonomous driving systems. The purpose of this study is to discuss the current progress using different approaches.
Anonymous Retrievable Payment from Public-key Searchable Encryption

Zerocash provides decentralized anonymous payment scheme from bitcoin. It tackles the privacy issues of senders, receivers and transaction contents. It is believed that Zerocash is a full-fledged privacy preserving digital currency. However, when payment are finished, receivers need to scan the encrypted transactions on public ledger. Apparently, scanning on public ledger directly takes time linear with total ciphertexts. When the amount of data is large, the search time is not efficient for users. To solve this problem, we take advantage of modified searchable public- key ciphertexts with hidden structures (SPCHS). Compared with other classical schemes, it significantly improves the search efficiency. Search time over the former is linear to the number of the total ciphertexts, while over the latter only linear to the number of related ciphertexts. With the building block of SPCHS, we propose a notion called decentralized anonymous payment with searchable transactions (DAPST) and give an instance of DAPST called Zerocash payment with efficient coin-retrieval (ZPECR).
Information system of the university editorial-publishing complex

This article presents the results of designing the structure of the data models necessary for the proper functioning of a database supporting the editorial, proofreading, printing, and publishing activities of an educational organization. The goal of the study is to create a coherent, objective, holistic, and non-redundant data set appropriate to the subject area to support the process of digital transformation of the publishing and publication sphere within an educational organization. The resulting data models reflect the logical structure of databases independent of the choice of a particular database management system. As part of the design, JavaScript Object Notation models and relational data models are developed to create a relational database. The results allow identifying and estimating the resources needed to implement each process, determining all participants affecting the implementation of the relevant processes, and defining the access to resources needed by certain participants in the process.
Mixed noise removal based on Stokes residual noise removal for division of focal plane polarimetric images

The division of focal plane (DoFP) polarimeter has the advantages of simultaneous imaging and compact optical structure. It is inevitable to introducing the noise in the process of micro-polarizer array integration, polarization image acquisition and transmission. In this paper, we propose a novel mixed noise suppression method based on Stokes residual noise removal (SRNR) for mixed additive white Gaussian noise (AWGN) and impulse noise (IN) removal in DoFP polarimetric images. The Laplacian scale mixture (LSM) model is introduced to estimate the IN and the nonlocal low-rank regularization (NLR) is adopted to enhance the denoising performance. The residual noise of the Stokes parameters is taken into consideration. This crucial processing achieves the acquisition of high-quality polarization data. The experiments demonstrate the superior denoising performance for polarimetric imagery in terms of objective assessment and visual evaluation.
Fast Estimation of Power Grid Operation State Based on Multi-source Data

The rapid change and uncertainty of power grid operation state put-forward higher requirements for the basic data of online calculation. A fast estimation method of grid operation state based on multi-source data is proposed. Based on the different characteristics of multi-source heterogeneous data, a unified data storage method is established to realize efficient storage and access of different data sources. A self-check and correction method of PMU and RTU based on historical measurement error is proposed to reduce the influence of abnormal data on data integration accuracy. A multi-source data fusion method is utilized to realize the rapid generation of sectional-power flow and topological information at the current moment. The correctness and feasibility of the proposed method are verified based on the actual power grid.
(m, N) Threshold Multi-signature Scheme Based on ECC Over Finite Field GF(q)

This paper expounds the invertibility of Vandermonde matrix over GF(q) field, the algorithm of matrix inversion and the time complexity, and then design a (m, N) threshold multi-signature scheme based on Ellipse Curve Cryptography (ECC, Ellipse Curve Cryptography) algorithm. In this scheme, no secret communication is required in the signature-issuing phase, verification contrast value is constant, and signature verification does not require the participation of TC(Trusted Center). Theoretical analysis shows that the scheme is safe; the test results prove the correctness of the algorithm and the analysis of computational time complexity (verification of signature and finite field matrix inversion). This multi-signature solution is especially suitable for multi-signature voting in application scenarios such as blockchain and smart contracts.
Hybrid Model Compression for Multi-Task Network

Abstract In recent years, deep neural networks have made significant achievements in various fields in science and engineering. To achieve outstanding performance, neural network models with very deep structures and an extensive number of parameters are usually used, leading to huge memory storage and computational resources requirements of the hardware systems, and thus preventing them from being deployed in devices with hardware limits. In particular, such problem will be further amplified when multiple neural network models for several tasks are to be integrated into one single platform at the same time. In this paper, a hybrid joint-network optimization model is proposed to solve this multi-task multi-model compression problem. The idea of hard parameter sharing in multi-task learning is adopted. Specifically, we turn the sub-network of the same structure of multiple models into a hybrid network model, in which the same parameters are shared for all the tasks. In addition, a unique sparse matrix for each task is added to each task independently. Experimental results show that the proposed compression methods can not only achieve a high compression ratio but also get performance improvement for some tasks.
A novel blockchain for social internet of vehicles in the Era of 5G and Beyond

The Social Internet of Vehicles (SIoV) paradigm is a social network where every vehicle establishes social relationships with others. The IEEE Standards Association develops a number of market relevent standards for for 5G and Beyond. Among these standards, the IEEE P1931.1 standard provides a promising architectural framework for SIoV. In this paper we propose RoofCoin, a blockchain for SIOV based on the IEEE P1931.1 standard. In order to deal with constrains related to Internet of Vehicles, we also provide a novel consensus protocol for RoofCoin, named PoS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> (Proof of Stature). In PoS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> , the blockchain’s miners are selected according to three features: the mining price, the reliability of the connection link between the vehicle and the miner, and the miner’s reputation. In order to evaluate the miner’s reputation, we introduce a trust model that calculate the miner’s stature in the Vehicular Social Network (VSN). Experimental results have shown that the proposed PoS <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</sup> protocol is efficient even in low coverage zones, where only two trusted miners are discovered.
Evolutionary Model Owner Selection for Federated Learning with Heterogeneous Privacy Budgets

Leveraging on the wealth of data and advancements in Artificial Intelligence, smart cities have demonstrated their great potential in providing solutions to challenges that the urban population faces today. However, as the urban population becomes more privacy sensitive and with the introduction of stringent privacy regulations, the differential-private FL (DPFL) is a promising technology that can enable privacy-preserving collaborative model training. In this paper, we consider an FL network of model owners and data owners with heterogeneous privacy budgets and preferences respectively. In exchange for their participation in the training, the model owner offers a reward pool that is shared among the data owners that take part in the FL training. In turn, the FL worker with heterogeneous privacy preferences may select the model owner to contribute its parameters to. To model the dynamic and strategic behaviour of the workers in the process of model owner selection, we propose an evolutionary game approach. Then, we conduct simulations to validate the evolutionary equilibrium, as well as provide the sensitivity analyses of the model.
New Designed Technology-Based Textbook of Indonesian for Foreign Speakers (BIPA)

BIPA learning for academic purposes for Chinese students has been going quite well so far. The implementation of this program has not been optimal, one of which is due to the absence of teaching materials that are ready to be used. Teaching materials are one of the components that determine the success of learning. The absence of adequate teaching materials has caused BIPA learning for academic purposes to seem sporadic. Moreover, Chinese students have language and cultural characteristics that are different from students in other programs, so Chinese students cannot make use of teaching materials commonly used in other BIPA programs. For this reason, an effort is needed to develop BIPA learning teaching materials for academic purposes. The model for developing teaching materials used in this study is the Borg and Gall model. The results of this study are in the form of teaching materials BIPA for academic purposes for intermediate-level learners. The results showed that the developed teaching materials were feasible to be implemented with an average score of 91.25.
THE CORRELATION BETWEEN STUDENTS’ READING HABIT AND READING COMPREHENSION OF THE TWELFTH GRADE IN SMA N 01 KOTABUMI NORTH LAMPUNG ACADEMIC YEAR 2021/2022

This research intends to investigate the phenomenon of students' reading habit that can be related to their reading comprehension. The purpose of this study is to find out whether there is a significant correlation between students’ reading habit and reading comprehension of the Twelfth grade in SMA N 01 Kotabumi North Lampung Academic year 2021/2022. This type of research is quantitative research and uses a correlational research design. The population consists of 142 students. Sample were taken from 15% of the population from each class so that the number of samples was 34 students. The instrument used in this study was a questionnaire and multiple choice test. Then, to fulfill the hypothesis, the researcher analyzed the data using the Pearson product moment correlation formula and obtained the observed 0.532, so the value of tobserved &gt; ttable was 3.556&gt; 2.037. Based on the results of data analysis, it is concluded that H0 is rejected and Ha is accepted, meaning that there is a significant correlation between reading habit and reading comprehension.