Reviewerly/exHarmony huggingface dataset:
Design of Graph Filter Based on Transformation Matrix and Spectral Response

In this paper, designs of polynomial graph filters based on transformation matrix and spectral response are presented. In the literature, there are two methods to describe the given filter specifications. One is the desired transformation matrix; the other is the ideal spectral response. This paper aims to study the least-squares (LS) filter designs using these two different specifications and to determine which design gives the better performance. As a result, the transformation matrix method provides smaller approximation error, but it is a graph- dependent design. If graph topology is changed, the filter coefficients need to be re-designed. For the spectral response method, it is a graph-independent design though it has slightly larger approximation error than transformation matrix method. Finally, the real temperature data of sensor network is used to evaluate the performance of these two design methods in terms of the approximation error.
Simulation of chlorophyll-a concentration in Donghu Lake based on GA-ELM and multiple water quality indexes

Simulation of chlorophyll-a concentration is an important method for monitoring lake eutrophication. In recent years, plenty of achievements have made in the study on chlorophyll-a concentration inversion model and water quality numerical model. However, the application of these two kinds of models is limited in lakes water with small data sample. In this paper, building non-mechanism model based on the relationship between chlorophyll-a concentration and multiple water quality indexes is proved to be feasible, at the same time, linear regression (LR) model, extreme learning machine (ELM) model, and ELM optimized by genetic algorithm (GA-ELM) model were built to simulate chlorophyll-a concentration. The simulation results show that the simulation effect of ELM model is better than that of LR model, and GA-ELM model works best. In addition, genetic algorithm (GA) effectively improves the simulation effect of ELM, R<sup>2</sup> of GA-ELM model increased by 0.1570, RMSE decreased by 7.12 μg/L, MAPE decreased by 0.7117 when compared with ELM model. The study provides a simple and effective method to simulate chlorophyll-a concentration, which can be applied for practical monitoring eutrophication in Donghu Lake.
Brightness-gradient difference feature guided shadow removal method

Shadow removal is a fundamental and pivotal task to build the high-level cognition in the computer vision field. Due to the fact that the existing shadow removal methods cannot effectively remove shadows from the outdoor image and deal with shadow boundaries, we construct a convolutional neural network without the process of shadow detection for the shadow removal task. The constructed CNN avoids the risk of gradient vanishing by designing double-attention residual block and improves the performance of shadow removal by fusing the knowledge transfer idea. Specially, we design a hand-crafted feature, named brightness-gradient difference feature, to distinguish shadow boundary pixels from non-shadow boundary pixels, and the designed feature is fused into the loss function to dilute or even eliminate the existing shadow boundaries. Extensive experiments using three public shadow removal benchmarks with three measurable indicators are reported in this paper. The results of experiments demonstrate that the proposed method has an effective performance for the shadow removal task. The ablation studies validate the structural rationality of the proposed method.
Influence of Pitch Angle Errors in 3D Scene Reconstruction Based on U-V Disparity: A Sensitivity Study

U-V disparity is a technique that is commonly used to detect obstacles in 3D scenes, modeling them as a set of vertical planes. In this paper, the authors describe the general lines of a method based on this technique for fully reconstructing 3D scenes, and conduct an analytical study of its performance and sensitivity to errors in the pitch angle of the stereoscopic vision system. The equations of the planes calculated for a given error in this angle yield the deviation with respect to the ideal planes (with a zero error in the angle) for a large test set consisting of planes with different orientations, which is represented graphically to analyze the method's qualitative and quantitative performance. The relationship between the deviation of the planes and the error in the pitch angle is observed to be linear. Two major conclusions are drawn from this study: first, that the deviation between the calculated and ideal planes is always less than or equal to the error considered in the pitch angle; and second, that even though in some cases the deviation of the plane is zero or very small, the probability that a plane of the scene deviates from the ideal by the greatest amount possible, which matches the error in the pitch angle, is very high.
Research on automobile insurance fraud identification based on fuzzy association rules

With the development of automobile insurance industry, how to identify automobile insurance fraud from massive data becomes particularly important. The purpose of this paper is to improve automobile insurance fraud management and explore the application of data mining technology in automobile insurance fraud identification. To this aim, an Apriori algorithm based on simulated annealing genetic fuzzy C-means (SAGFCM-Apriori) have been proposed. The SAGFCM-Apriori algorithm combines fuzzy theory with association rule mining, expanding the application scope of the Apriori algorithm. Considering that the clustering center of the traditional fuzzy C-means (FCM) algorithm is easy to fall into local optimal, the simulated annealing genetic (SAG) algorithm is used to optimize it. The SAG algorithm optimized FCM (SAGFCM) is used to generate fuzzy membership degrees and introduces fuzzy data into the Apriori algorithm. The Apriori algorithm is improved by reducing the rule mining time when acquiring rules. The results of empirical studies on several data sets demonstrate that the optimization of FCM by SAG can effectively avoid the local optimal problem, improve the accuracy of clustering, and enable SAGFCM-Apriori to obtain better fuzzy data during data preprocessing. Moreover, the proposed algorithm can reduce the mining time of association rules and improve mining efficiency. Finally, the SAGFCM-Apriori algorithm is applied to the scene of automobile insurance fraud identification, and the automobile insurance fraud data is mined to obtain fuzzy association rules that can identify fraud claims.
LSTM-Based Prediction Method of Crowd Behavior for Robust to Pedestrian Detection Error

The traffic accidents at crossing the road occur due to the impossibility of predicting the behavior of pedestrians from vehicles or their drivers. In order to reduce the number of these accidents, a novel method for predicting the behavior of pedestrians needs to be constructed. Basic elements of the prediction of the pedestrian behavior are detecting and tracking of the pedestrians. However, the accuracy of pedestrian detection is reduced because pedestrians are hidden by each other or by various objects on the road. Therefore, utilization of the conventional object detection methods is limited in the actual road space. In this paper, a robust prediction method for the errors of pedestrian detection is proposed. The proposed method does not track each pedestrian individually, but tracks multiple pedestrians as a crowd. The effectiveness of the proposed method was confirmed by utilizing the actual crowd video.
Machine Learning for Social Sciences: Stance Classification of User Messages on a Migrant-Critical Discussion Forum

In this paper, we present our methodology for supervised stance classification of sparse and imbalanced social media data. We test our framework on a manually labeled dataset of 5700 messages about immigration in the Swedish language posted on the Flashback forum, a controversial online discussion platform. Our proposed approach currently achieves a macro- averaged F1-score of 0.72 for test data on a two-class problem compared against 0.27 for a baseline four-class model. Since effective classification of imbalanced and sparse textual data in under-resourced languages presents certain methodological challenges, our study contributes to a discussion on the best pathways to achieve highest model performance given the character of the data and unavailability of large training datasets for this task. Moreover, this work exemplifies the application of ML methodology to social media data, which can be particularly relevant for social scientists working in this area and interested in leveraging the possibilities of machine learning in their research field. This methodology and the obtained results provide a foundation for further in-depth analyses of social media texts in the Swedish language following a data-driven approach.
Measuring Objective Visual Quality of Real-time Communication Systems in the Wild

During the outbreak of epidemic diseases, the demand for real-time communication (RTC) systems has dramatically increased. People use RTC systems to communicate with each other, attend online courses, present projects, and share videos. An open question, how to choose an appropriate system for high-quality communication with different network conditions and usage scenarios, comes RTC users. In this paper, we propose a scheme to systematically measure the visual quality of RTC systems. Our scheme does not need to modify systems and thus can be easily adapted to other RTC systems and video transmission systems, i.e., streaming systems. Based on this scheme, we measure six commercial RTC systems in four common usage scenarios. We measure the received video quality (graphical quality and frame rate) at the receiver and the upload bitrate at the sender. Furthermore, we propose a novel metric, area loss, to measure the system ability to handle insufficient bandwidth using video freezing and frame delay. Our detailed analysis reveals the advantages and disadvantages of each system. We expect the measurement scheme, the metric, and our findings can help the future development of RTC systems.
Acquisition challenges for high quality data using a UAV deployed magnetometer

PreviousNext No AccessSixth International Conference on Engineering Geophysics, Virtual, 25–28 October 2021Acquisition challenges for high quality data using a UAV deployed magnetometerAuthors: Christopher LeechStefan BurnsKevin HurleyChristopher LeechGeomatrix Earth Science Ltd.Search for more papers by this author, Stefan BurnsGeometrics Inc.Search for more papers by this author, and Kevin HurleyGeometrics Inc.Search for more papers by this authorhttps://doi.org/10.1190/iceg2021-046.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Geophysical surveys using UAV are becoming increasingly common as for many surveys they are more efficient or indeed required due to ground conditions and other hazards such as presence of UXO. Geophysical UAV surveying presents its own challenges though in order to achieve the best quality data. This presentation discusses some of the challenges surrounding UAV deployed magnetometers in acquiring high quality magnetic data. Keywords: magnetometer, noise, sensors, sampling, electromagneticPermalink: https://doi.org/10.1190/iceg2021-046.1FiguresReferencesRelatedDetails Sixth International Conference on Engineering Geophysics, Virtual, 25–28 October 2021ISSN (online):2159-6832Copyright: 2021 Pages: 341 publication data© 2021 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished Online: 24 Dec 2021 CITATION INFORMATION Christopher Leech, Stefan Burns, and Kevin Hurley, (2021), "Acquisition challenges for high quality data using a UAV deployed magnetometer," SEG Global Meeting Abstracts : 173-176. https://doi.org/10.1190/iceg2021-046.1 Plain-Language Summary KeywordsmagnetometernoisesensorssamplingelectromagneticPDF DownloadLoading ...
A Two-Step TLS-Based Browser fingerprinting approach using combinatorial sequences

We propose a two-step TLS-based fingerprinting approach using combinatorial sequences and properties of TLS handshake messages. Our approach combines fingerprinting based on attributes of the initial ClientHello message with the observed behavior of TLS clients when presented with permuted handshake messages in order to enhance the granularity of the derived fingerprints without increasing the required number of exchanged messages. We conduct a detailed evaluation against 21 browsers and TLS clients on two operating systems. The results show a significant increase in the entropy of the achieved splittings, allowing for a more precise identification of the TLS client than permitted by either of the underlying approaches in isolation.
Causal and Masked Language Modeling of Javanese Language using Transformer-based Architectures

Most natural language understanding breakthroughs occur in popularly spoken languages, while low-resource languages are rarely examined. We pre-trained as well as compared different Transformer-based architectures on the Javanese language. They were trained on causal and masked language modeling tasks, with Javanese Wikipedia documents as corpus, and could then be fine-tuned to downstream natural language understanding tasks. To speed up pre-training, we transferred English word-embeddings, utilized gradual unfreezing of layers, and applied discriminative fine-tuning. We further fine-tuned our models to classify binary movie reviews and find that they were on par with multilingual/cross-lingual Transformers. We release our pre-trained models for others to use, in hopes of encouraging other researchers to work on low-resource languages like Javanese.
Blockchain-Enabled Clustered Federated Learning in Fog Computing Networks

In mobile computing scenarios, federation learning allows users to jointly train global models in a decentralized manner without exposing private data. However, due to the heterogeneity of the network and devices, the traditional global model often fails to fit the user data distribution, which is inconsistent with the primary condition of federation learning, resulting in accuracy decreasing of global models. Besides, the security of federated learning is decreasing with the increase of malicious attacks. To address the aforementioned issues, in this paper, we explore the cosine similarity of model gradients and design a clustered mechanism to improve learning efficiency. Furthermore, we combine the clustered federated learning with the blockchain-supported fog computing networks, which could verify local models uploaded by users and generate the traceable global models to improve the learning efficiency. Finally, we conduct experiments on several frameworks with the real-world dataset FEMNIST, and the experimental results demonstrate the efficiency and robustness of the blockchain-enabled clustered federated learning framework.
Design and Implementation of Three Application Software Employed in Web Based Rain Gauge Calibrators

A Web-Based Rain Gauge Calibrator (WBRGC) is a rain gauge calibrator where the calibration result can be downloaded from a specific web as soon as the WBRGC finish the calibration process. This study aimed to design and implement three application software, namely data logger, display, and web application software used in WBRGC. This study was carried out by assessing their performances during the implementation of three application software along a calibration process. The data from fieldwork, namely ambient temperature, ambient humidity, water temperature, calibration duration, and the number of tipping, is sent by the data logger unit to the web unit through the display unit. The result shows that the software worked well for data transfers between the data logger application software and the display application software; and between the display application software and the web application software. It means that all software can operate well to run the WBRGC according to its function.
An efficient approach for multiple probabilistic inferences with Deepwalk based Bayesian network embedding

As a classical probabilistic graphic model, Bayesian network (BN) is widely used for representing and inferring dependence relationships with uncertainties. However, multiple probabilistic inferences on BN are quite inefficient, since each probabilistic inference on BN is extremely time-consuming and meanwhile the intermediate results cannot be reused. It is necessary to improve the overall efficiency of multiple probabilistic inferences on the same BN by incorporating an easy-to-calculate inference method and an easy-to-reuse technique for common calculations in multiple inference tasks. In this paper, we first propose a Deepwalk based method for BN embedding, as a specification of general graph embedding, which preserves both the graphical structure and conditional probabilities in BN. We then provide the algorithm to approximate probabilistic inferences via the distance among embeddings. We finally present an efficient approach to multiple probabilistic inferences. Extensive experiments illustrate that our proposed method is effective for BN embedding, and outperforms other state-of-the-art competitors by improving the inference efficiency with several orders of magnitude.
Chaotic Particle Swarm Optimization Based on Meeting Room Approach for Designing Bijective S-Boxes

This paper proposed a modified and enhanced multi-swarm Particle Swarm Optimization algorithm based on Meeting Room Approach (MPSO) with Tent Logistic map. The proposed modified MPSO algorithm was used to generate strong 8 × 8 substitution boxes (S-Box) while a Chaotic Tent map was used to update the velocities of each leader in the meeting room to improve their movements and enhance the exploration capability of the MPSO. The generated S-box was validated using cryptographic performance evaluation criteria, such as nonlinearity, bijectivity, strict avalanche criteria, bit independence criteria, differential probability, and linear approximation probability. The obtained results showed that the proposed algorithm performed better than some of the state-of-the-art algorithms and proved its capability against cryptanalysis attacks.
A Comprehensive Study on Enhanced Clustering Technique of Association Rules over Transactional Datasets

The most well-recognized fields in data mining is association rule mining. It’s been used within various applications including industry baskets, computer networks, recommendation systems and healthcare. Exploratory data analysis and data mining (DM) applications rely heavily on clustering. Cluster analysis seeks to categorize a group of patterns into groups based on their similarity. This paper aims to enhance the clustering technique of association rules over transactional datasets. At the outset the concepts behind association rules are explained followed by an overview of some of the recent research in this field. The benefits and drawbacks are addressed and a conclusion is drawn.
Features of Digital Student Generation Education: from Theory to Practice

This article summarizes the learning characteristics of the digital generation of students, the emerging field of digital pedagogy and didactics. Digital didactics is based on the concepts of Connectivism and Heutagogy, Actor-network theory, Rhizomatic education approaches, etc. Researchers highlight the following characteristics of the digital generation: clip thinking, shifting attention, multitasking, heavy short-term memory load, digital youth slang, need for praise, and minimal planning horizon, etc. A survey of 142 students from four universities in Kazakhstan was conducted on the characteristics of the digital generation of students. It was found that 90.8% of the sample of students identify themselves as belonging to the digital generation; when learning, of the three sources of information, students prefer to choose first: 50.7% - electronic textbook, 38.7% - video materials, 37.3% - paper textbook. At the same time, only a third of the students gave a detailed answer as to what clip thinking is, some students found it difficult to answer or were not aware of the phenomenon.
Lightweight Deep Learning Applications on AVX-512

Machine Learning and Deep Learning applications are of paramount importance these days. Different areas of academia and industry use daily workloads based on these applications. Several aspects are relevant regarding their applicability, such as the complexity and accuracy of the models and their performance and energy efficiency. Currently, there is a trend to usually favor the use of GPUs to train and execute Deep Learning models, intensified by specialized hardware. However, this article demonstrates that using a CPU with AVX-512 instructions can achieve comparable performance to current GPUs and, depending on the workload, suppress it by ≈ 1.8x.
Blind Vision and Theft Protection Device in Vehicles using Embedded Systems

The overall purpose of the research is to reduce the number of accidents by constructing a device with the assistance of the embedded systems and the Internet of Things, keeping vehicle security and safety of the driver a priority, which can be installed in any vehicle without actually changing its Generic manufacturing.The problems faced by people have been taken into consideration like utilising the necessary tools, features, functions required to reduce the alarming rate of accidents. The device will consist of features like automatic detection of the distance of vehicles in front when they appear in the close range, Automatic brake triggering assembly setup, alert system. Some additional features like a live tracking facility, WIFI Fidelity in vehicles, and dashboard camera monitoring.These elements will enhance the security of the vehicle and prevent theft. The enviable advantage of this system is that it helps the owner in tracking the vehicle at a greater pace, and reduces the complexities compared to other systems, besides being the cheapest and safest alternative for an anti-theft system as well.
Key Space Reduction Using Isomorphs

Block ciphers are said to have key spaces that are large enough to prevent a brute force attack from breaking them within the lifetime of the attacker; however, messages obscured using those ciphers are regularly broken. Some are broken because of increased computer capabilities, while others are broken because of industry reliance on invalid assumptions. These assumptions include the idea that all encrypted blocks of text are equally likely to appear in a message and that only the original key can decrypt a message. In this paper, we show that information theory techniques using isomorphs can reduce the key space to a size that makes the subsequent brute force attack possible in a shorter and easily achievable time frame.
Evaluating the Effectiveness of Software Development Processes Based on Probabilistic Causal Models

An Agile methodology for developing software Agile is considered from the point of view of the effectiveness of practical implementation. An approach to assessing the effectiveness of project activities, presented in the form of generalized scenarios, is described. Such scenarios are built on the idea of a causal relationship between the stages of a scenario. The implementation of the Agile approach in the form of a generalized scenario is considered. A method is proposed for assessing the effectiveness of activities for the implementation of scenarios, which can be considered as generalized design solutions based on invariant criteria. For this purpose, a mathematical apparatus based on Markov chains is used. To implement the methodology, software was implemented using modern tools as part of the Python language, the Qt graphical interface library and the NetworkX graph visualization library. An example of evaluating the effectiveness of the software development process within one sprint within the framework of Agile technology is considered.