Reviewerly/exHarmony huggingface dataset:
Path-scan: A novel clustering algorithm based on core points and connexity

A new clustering algorithm Path-scan aiming at discovering natural partitions is proposed. It is based on the idea that a (k,ɛ) coreset of the original data base represented by core and support patterns can be path-connected via a density differential approach. The Path-scan algorithm is structured in two main parts producing a connectivity matrix where partitions can be extracted at different levels of granularity. The first one aims to identify and select core and support points while the second one extracts connected components of core points and clusters with the help of support points. A simulation experiment based on synthetic and real world data sets was conducted to show the effectiveness of the proposed method.
Proposals Generation for Weakly Supervised Object Detection in Artwork Images

Object Detection requires many precise annotations, which are available for natural images but not for many non-natural data sets such as artworks data sets. A solution is using Weakly Supervised Object Detection (WSOD) techniques that learn accurate object localization from image-level labels. Studies have demonstrated that state-of-the-art end-to-end architectures may not be suitable for domains in which images or classes sensibly differ from those used to pre-train networks. This paper presents a novel two-stage Weakly Supervised Object Detection approach for obtaining accurate bounding boxes on non-natural data sets. The proposed method exploits existing classification knowledge to generate pseudo-ground truth bounding boxes from Class Activation Maps (CAMs). The automatically generated annotations are used to train a robust Faster R-CNN object detector. Quantitative and qualitative analysis shows that bounding boxes generated from CAMs can compensate for the lack of manually annotated ground truth (GT) and that an object detector, trained with such pseudo-GT, surpasses end-to-end WSOD state-of-the-art methods on ArtDL 2.0 (≈41.5% mAP) and IconArt (≈17% mAP), two artworks data sets. The proposed solution is a step towards the computer-aided study of non-natural images and opens the way to more advanced tasks, e.g., automatic artwork image captioning for digital archive applications.
Analyzing the impact of a real-life outbreak simulator on pandemic mitigation: An epidemiological modeling study

An app-based educational outbreak simulator, Operation Outbreak (OO), seeks to engage and educate participants to better respond to outbreaks. Here, we examine the utility of OO for understanding epidemiological dynamics. The OO app enables experience-based learning about outbreaks, spreading a virtual pathogen via Bluetooth among participating smartphones. Deployed at many colleges and in other settings, OO collects anonymized spatiotemporal data, including the time and duration of the contacts among participants of the simulation. We report the distribution, timing, duration, and connectedness of student social contacts at two university deployments and uncover cryptic transmission pathways through individuals’ second-degree contacts. We then construct epidemiological models based on the OO-generated contact networks to predict the transmission pathways of hypothetical pathogens with varying reproductive numbers. Finally, we demonstrate that the granularity of OO data enables institutions to mitigate outbreaks by proactively and strategically testing and/or vaccinating individuals based on individual social interaction levels.
Learning Supplementary NLP Features for CTR Prediction in Sponsored Search

In sponsored search engines, pre-trained language models have shown promising performance improvements on Click-Through-Rate (CTR) prediction. A widely used approach for utilizing pre-trained language models in CTR prediction consists of fine-tuning the language models with click labels and early stopping on peak value of the obtained Area Under the ROC Curve (AUC). Thereafter the output of these fine-tuned models, i.e., the final score or intermediate embedding generated by language model, is used as a new Natural Language Processing (NLP) feature into CTR prediction baseline. This cascade approach avoids complicating the CTR prediction baseline, while keeping flexibility and agility. However, we show in this work that calibrating separately the language model based on the peak single model AUC does not always yield NLP features that give the best performance in CTR prediction model ultimately. Our analysis reveals that the misalignment is due to overlap and redundancy between the new NLP features and the existing features in CTR prediction baseline. In other words, the NLP features can improve CTR prediction better if such overlap can be reduced.
Exploring Machine Learning for Classification of QUIC Flows over Satellite

Automatic traffic classification is increasingly important in networking due to the current trend of encrypting transport information (e.g., behind HTTP encrypted tunnels) which prevent intermediate nodes to access end-to-end transport headers. This paper proposes an architecture for supporting Quality of Service (QoS) in hybrid terrestrial and SATCOM networks based on automated traffic classification. Traffic profiles are constructed by machine-learning (ML) algorithms using the series of packet sizes and arrival times of QUIC connections. Thus, the proposed QoS method does not require explicit setup of a path (i.e. it provides soft QoS), but employs agents within the network to verify that flows conform to a given traffic profile. Results over a range of ML models encourage integrating ML technology in SATCOM equipment. The availability of higher computation power at low-cost creates the fertile ground for implementation of these techniques.
SOKNL: A novel way of integrating K-nearest neighbours with adaptive random forest regression for data streams

Abstract Most research in machine learning for data streams has focused on classification algorithms, whereas regression methods have received a lot less attention. This paper proposes Self-Optimising K-Nearest Leaves (SOKNL), a novel forest-based algorithm for streaming regression problems. Specifically, the Adaptive Random Forest Regression, a state-of-the-art online regression algorithm is extended like this: in each leaf, a representative data point – also called centroid – is generated by compressing the information from all instances in that leaf. During the prediction step, instead of letting all trees in the forest participate, the distances between the input instance and all centroids from relevant leaves are calculated, only k trees that possess the smallest distances are utilised for the prediction. Furthermore, we simplify the algorithm by introducing a mechanism for tuning the k values, which is dynamically and automatically optimised based on historical information. This new algorithm produces promising predictive results and achieves a superior ranking according to statistical testing when compared with several standard stream regression methods over typical benchmark datasets. This improvement incurs only a small increase in runtime and memory consumption over the basic Adaptive Random Forest Regressor.
Assessing Human Motion During Exercise Using Machine Learning: A Literature Review

The World Health Organization promotes healthy living through regular physical activities, such as exercise and sports, as well as access to healthcare and rehabilitation services for people with motor dysfunctions. However, there is a lack of specialized personnel and increased costs associated with such activities. These have led to the increased use of machine learning for the analysis and evaluation of human motion during exercise. To study the latest advancements in this area, a systematic literature review focusing on publications from 2017 to 2021 was performed. As a result, 88 relevant publications were identified, which developed both shallow machine learning and deep learning algorithms. The results indicated that algorithms for human motion assessment should provide personalized and informative assessments, with explainable and interpretable outcomes, that can be computed in real-time or concurrently with the execution of an exercise. Furthermore, they should be easy to adapt based on the needs of applications and should be able to perform with different motion capture systems. This has been challenging because of the usually small amount of collected data, the lack of large open datasets, and the unique characteristics of exercise motions. Based on the above findings, guidelines for the development of such algorithms are proposed and discussed. They relate to the selection of the type of assessment, handling data imbalances, selecting of motion capture technologies, balancing between accuracy and speed, selecting the right algorithm, performing concurrent assessment during an exercise, personalization and scalability, and evaluation.
Mapping mineralogical heterogeneities at the nm-scale by scanning electron microscopy in modern Sardinian stromatolites: Deciphering the origin of their laminations

Stromatolites are found throughout the geological record and have received strong attention because they provide precious information about paleoenvironments and microbial paleobiodiversity. However, while this information is interpreted based on our knowledge about modern analogs, the latter remains incomplete. Here, we investigated the environmental and biological information recorded by modern stromatolites in Mari Ermi, a coastal pond in Western Sardinia that experiences severe seasonal evaporation and large salinity variations. For this purpose, we combined a variety of analytical tools, allowing to characterize the mineralogical composition of these stromatolites from the bulk, centimeter-scale to the nanometer-scale and assess the spatial distribution of the mineral phases. In particular, we used quantified x-ray elemental maps provided by energy dispersive x-ray spectrometry analyses coupled with scanning electron microscopy (SEM-EDXS). These maps were processed using an innovative data treatment allowing (i) mineral phase recognition, (ii) assessment of the mineral formula of each phase and (iii) mapping of their spatial distribution with a spatial resolution down to a hundred nanometers. Overall, this proved as an efficient approach to unravel mineralogical heterogeneities and detect informative mineral phases overlooked by bulk analyses. Mari Ermi stromatolites were mostly composed of magnesian calcite with an average Mg/Ca of ~0.1–0.2 as indicated by bulk analyses. However, microscopy observations revealed variable Mg-contents of calcite with a specific distribution, the most Mg-enriched calcites (Mg/Ca up to ~0.5) being systematically distributed around microbial remnants. Moreover, Mari Ermi stromatolites comprised an alternation of Mg-richer and Mg-poorer laminae, which paralleled the varying enrichment in microbial remnants of alternating laminae. The combination of SEM-EDXS, focused ion beam milling and transmission electron microscopy allowed to detect several minor phases, hardly or not detected at all by bulk analyses, such as aragonite, a variety of clay minerals as well as a gypsum-like phase. An additional calcium sulfate phase, best interpreted as S-apatite (cesanite), was detected by SEM-EDXS. Overall, this mineral assemblage and its definite spatial distribution record only discrete stages of the evaporation of Mari Ermi lagoon. Moreover, microorganisms seem to have a major control on Mg substitution in calcite. As a result, the dynamics of microbial populations, influenced by the salinity variations induced by evaporation in the lagoons, generates the formation of laminae in Mari Ermi stromatolites.
Multi-Scale Object Detection with the Pixel Attention Mechanism in a Complex Background

The object detection task is usually affected by complex backgrounds. In this paper, a new image object detection method is proposed, which can perform multi-feature selection on multi-scale feature maps. By this method, a bidirectional multi-scale feature fusion network was designed to fuse semantic features and shallow features to improve the detection effects of small objects in complex backgrounds. When the shallow features are transferred to the top layer, a bottom-up path is added to reduce the number of network layers experienced by the feature fusion network, reducing the loss of shallow features. In addition, a multi-feature selection module based on the attention mechanism is used to minimize the interference of useless information in subsequent classification and regression, allowing the network to adaptively focus on appropriate information for classification or regression to improve detection accuracy. Because the traditional five-parameter regression method has severe boundary problems when predicting objects with large aspect ratios, the proposed network treats angle prediction as a classification task. The experimental results on the DOTA dataset, the self-made DOTA-GF dataset and the HRSC 2016 dataset show that, compared with several popular object detection algorithms, the proposed method has certain advantages in detection accuracy.
Hybrid Bayesian Network-Based Modeling: COVID-19-Pneumonia Case

The primary goal of this paper is to develop an approach for predicting important clinical indicators, which can be used to improve treatment. Using mathematical predictive modeling algorithms, we examined the course of COVID-19-based pneumonia (CP) with inpatient treatment. Algorithms used include dynamic and ordinary Bayesian networks (OBN and DBN), popular ML algorithms, the state-of-the-art auto ML approach and our new hybrid method based on DBN and auto ML approaches. Predictive targets include treatment outcomes, length of stay, dynamics of disease severity indicators, and facts of prescribed drugs for different time intervals of observation. Models are validated using expert knowledge, current clinical recommendations, preceding research and classic predictive metrics. The characteristics of the best models are as follows: MAE of 3.6 days of predicting LOS (DBN plus FEDOT auto ML framework), 0.87 accuracy of predicting treatment outcome (OBN); 0.98 F1 score for predicting facts of prescribed drug (DBN). Moreover, the advantage of the proposed approach is Bayesian network-based interpretability, which is very important in the medical field. After the validation of other CP datasets for other hospitals, the proposed models can be used as part of the decision support systems for improving COVID-19-based pneumonia treatment. Another important finding is the significant differences between COVID-19 and non-COVID-19 pneumonia.
Articulate Storyline 3 multimedia based on gamification to improve critical thinking skills and self-regulated learning

&lt;span lang="EN-US"&gt;This study measured the effectiveness of Gamification-based Articulate Storyline 3 multimedia on students' critical thinking skills and self-regulated learning. The research method used is a quasi-experimental method. The research subjects were 64 students and eight science teachers who have used the Gamification-based Articulate Storyline 3 multimedia on the nature of acidic, alkaline, and neutral solutions. Data collection techniques in this study were tests that aim to determine students' critical thinking skills and a questionnaire to measure students' learning independence. The data analysis technique was based on calculating the average score of the test items with high-order thinking skills and student learning independence. The study results obtained an average score of 81.50 for critical thinking skills on the material concept of the nature of the acid, base, and neutral solutions with an N-Gain of 72% in the high category. It was also known that the average percentage of student learning independence was 86.76% which falls into the good category. Therefore, Gamification-based Articulate Storyline 3 multimedia can improve students' critical thinking skills and independent learning on the nature of the acid, base, and neutral solutions.&lt;/span&gt;
Deep Tensor Evidence Fusion Network for Sentiment Classification

Recently, a multimodal sentiment analysis of social media has attracted increasing attention, and its core idea is to discovery heuristic fusion strategy to analyze the sentiment orientations over heterogeneous multimodal source from a learned compact multimodal representation. The existing multimodal fusion techniques not only struggle to achieve full heterogeneous data interaction, but also they are unable to dynamically assess the quality of various modal data to determine predictability. In this article, we present a novel deep tensor evidence fusion (DTEF) network for multimodal sentiment classification. First, we propose a common view evaluation network that uses a long short-term memory (LSTM) network and a tensor-based neural network to extract rich intermodal and intramodal information. Then, we propose a unique time cue evaluation network that takes advantage of the temporal granularity associated with numerous pattern sequences. To make reliable decisions, we finally incorporate uncertainty through the trusted fusion layer, which improves the accuracy and robustness of sentimental classification. Our model is validated using the CMU Multimodal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) and CMU Multimodal Corpus of Sentiment Intensity (CMU-MOSI) datasets, and the experimental findings demonstrate the superior performance of the proposed network in terms of accuracy compared with the state-of-the-art methods.
Network Media Public Opinion and Social Governance Supported by the Internet-of-Things Big Data

The purpose is to make for the traditional Network Public Opinion (NPO) analysis methods’ inadequacy in the era of big data and provide a sufficient decision-making basis for managers. Based on the Internet of Things (IoT) and big data, this work applies Natural Language Processing (NLP) to NPO analysis. Additionally, it takes the content of Microblog text format as the main collection target, constructs a big data collection tool, and establishes Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Deep Pyramid Convolutional Neural Network (DPCNN) based on Tensorflow and other deep learning models. It is also improved in combination with the characteristics of the model, and a new model is proposed. Finally, the performance of various models is compared and analyzed through experiments, and the path is proposed for the government to use big data to improve the ability to govern NPO and help social governance. The results show that the improved LSTM model can correctly classify the extracted Microblog text’s emotion by as much as 80.00%. It improves the classification accuracy by nearly two percentage points under the ideal condition. Thus, by adding residual connection and attention mechanism, the model can extract the emotional features in the text better and improve the emotional discrimination ability. The public opinion of online media without effective control will have great security risks to social governance under the big data and IoT. The proposed method is of great help in analyzing NPO through the accurate analysis of Microblog text.
EPI Light Field Depth Estimation Based on a Directional Relationship Model and Multiviewpoint Attention Mechanism

Light field (LF) image depth estimation is a critical technique for LF-related applications such as 3D reconstruction, target detection, and tracking. The refocusing property of LF images provide rich information for depth estimations; however, it is still challenging in cases of occlusion regions, edge regions, noise interference, etc. The epipolar plane image (EPI) of LF can effectively deal with the depth estimation because of its characteristics of multidirectionality and pixel consistency-in which the LF depth estimations are converted to calculate the EPI slope. This paper proposed an EPI LF depth estimation algorithm based on a directional relationship model and attention mechanism. Unlike the subaperture LF depth estimation method, the proposed method takes EPIs as input images. Specifically, a directional relationship model was used to extract direction features of the horizontal and vertical EPIs, respectively. Then, a multiviewpoint attention mechanism combining channel attention and spatial attention is used to give more weight to the EPI slope information. Subsequently, multiple residual modules are used to eliminate the redundant features that interfere with the EPI slope information-in which a small stride convolution operation is used to avoid losing key EPI slope information. The experimental results revealed that the proposed algorithm outperformed the compared algorithms in terms of accuracy.
Applications of the Internet of Things (IoT) and Blockchain for Agriculture in Indonesia

The agricultural sector is a source of food, clean water, and natural medicine, which is very important for human life. Agricultural problems need to be handled by the massive use of robotics and artificial intelligence technology along the agricultural value chain from upstream to downstream (from land to consumer) with the help of satellites and drones for remote sensing. Precision Agriculture is a system where input, process, and output parameters are measured and controlled precisely and accurately to produce efficient and productive agriculture.
An Improved Kalman Filter Based on Long Short-Memory Recurrent Neural Network for Nonlinear Radar Target Tracking

The target tracking of nonlinear maneuvering radar in dense clutter environments is still an important but difficult problem to be solved effectively. Traditional solutions often rely on motion models and prior distributions. This paper presents a novel improved architecture of Kalman filter based on a recursive neural network, which combines the sequence learning of recurrent neural networks with the precise prediction of Kalman filter in an end-to-end manner. We employ three LSTM networks to model nonlinear motion equation, motion noise, and measurement noise, respectively, and learn their long-term dependence from a large amount of training data. They are then applied to the prediction and update process of Kalman filter to calculate the estimated target state. Our approach is able to address the tracking problem of nonlinear maneuvering radar target online end-to-end and does not require the motion models and prior distributions. Experimental results show that our method is more effective and faster than the traditional methods and more accurate than the method with LSTM network alone.
Autonomous jerk oscillator with sine nonlinearity and logistic map for sEMG encryption

Abstract This research discusses the dynamical behaviors of the autonomous jerk oscillator with sine nonlinearity (AJOSN) and its application to secure s-EMG (Surface ElectroMyoGraphic) data. The AJOSN has two or no-equilibrium points, and the stability of the two equilibrium points indicates that one is stable and the other is unstable. The AJOSN reveals fast-spiking and periodic bursting, relaxing and periodic oscillations, hidden chaotic attractors and coexisting attractors. The numerical analysis results are validated by the Field Programmable Gate Array (FPGA) implementation. Finally, the AJOSN’s chaotic behavior coupled with the logistic map is exploited to encrypt the s-EMG signals. The proposed encryption and decryption method consists of simultaneously encrypting the sEMG signal. The sEMG signal, once transformed into 2D as a grayscale image, undergoes first encryption based on the chaotic signal generated by the AJOSN (Autonomous jerk oscillator with sine nonlinearity) model. The encrypted signal obtained from this first part is called ‘Jerk encrypted signal.’ The second part is to encrypt the ‘Jerk Encrypted Signal’ by the chaotic logistics map. The encryption and decryption results obtained are quite encouraging and offer a great prospect.