Reviewerly/exHarmony huggingface dataset:
Roll Angle Estimation Algorithm of Geomagnetic/Gyro Combination Based on an Interacting Multiple-Model Kalman Filter

The knowledge of the geomagnetic and gyro information that can be used for projectile roll angle is decisive to apply trajectory correction and control law. In order to improve the measurement accuracy of projectile roll angle, an interacting multiple-model Kalman filter (IMMKF) algorithm using gyro angular rate information to geomagnetic sensor information is proposed. Firstly, the data acquisition module of the geomagnetic sensor and the gyroscope sensor is designed, and the test data of the sensors are obtained through the semiphysical experiments. Furthermore, according to the measurement accuracy of each sensor, the algorithm performs the IMMKF process on the geomagnetic/gyro information to get the roll angle. It can be proven by experiments and calculation results that the error of the roll angle obtained after processing by the IMMKF algorithm is close to 2°, which is better than the 5° calculated by adopting the Kalman filter directly with geomagnetic information.
Information Sufficiency via Fourier Expansion

We take an information-theoretic approach to identify nonlinear feature redundancies in unsupervised learning. We define a subset of features as sufficiently-informative when the joint entropy of all the input features equals that of the chosen subset. We argue that the rest of the features are redundant as all the accessible information about the data can be captured from sufficiently-informative features. Next, instead of directly estimating the entropy, we propose a Fourier-based characterization. For that, we develop a novel Fourier expansion on the Boolean cube incorporating correlated random variables. This generalization of the standard Fourier analysis is beyond product probability spaces. Based on our Fourier framework, we propose a measure of redundancy for features in the unsupervised settings. We then consider a variant of this measure with a search algorithm to reduce its computational complexity as low as <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$O$</tex> (nd) with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$n$</tex> being the number of samples and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$d$</tex> the number of features. Besides the theoretical justifications, we test our method on various real-world and synthetic datasets. Our numerical results demonstrate that the proposed method outperforms state-of-the-art feature selection techniques.
A fuzzy edge detector driven telegraph total variation model for image despeckling

&lt;p style='text-indent:20px;'&gt;Speckle noise suppression is a challenging and crucial pre-processing stage for higher-level image analysis. In this work, a new attempt has been made using telegraph total variation equation and fuzzy set theory for image despeckling. The intuitionistic fuzzy divergence function has been used to distinguish between edges and noise. To the best of the authors' knowledge, most of the studies on the multiplicative speckle noise removal process focus only on diffusion-based filters, and little attention has been paid to the study of fuzzy set theory. The proposed approach enjoys the benefits of both telegraph total variation equation and fuzzy edge detector, which is robust to noise and preserves image structural details. Moreover, we establish the existence and uniqueness of weak solutions of a regularized version of the present system using the Schauder fixed point theorem. With the proposed technique, despeckling is carried out on natural, real synthetic aperture radar, and real ultrasound images. The experimental results computed by the suggested method are reported, which are found better in terms of noise elimination and detail/edge preservation, concerning the existing approaches.&lt;/p&gt;
Topic Models Ensembles for AD-HOC Information Retrieval

Ad hoc information retrieval (ad hoc IR) is a challenging task consisting of ranking text documents for bag-of-words (BOW) queries. Classic approaches based on query and document text vectors use term-weighting functions to rank the documents. Some of these methods’ limitations consist of their inability to work with polysemic concepts. In addition, these methods introduce fake orthogonalities between semantically related words. To address these limitations, model-based IR approaches based on topics have been explored. Specifically, topic models based on Latent Dirichlet Allocation (LDA) allow building representations of text documents in the latent space of topics, the better modeling of polysemy and avoiding the generation of orthogonal representations between related terms. We extend LDA-based IR strategies using different ensemble strategies. Model selection obeys the ensemble learning paradigm, for which we test two successful approaches widely used in supervised learning. We study Boosting and Bagging techniques for topic models, using each model as a weak IR expert. Then, we merge the ranking lists obtained from each model using a simple but effective top-k list fusion approach. We show that our proposal strengthens the results in precision and recall, outperforming classic IR models and strong baselines based on topic models.
Translation of American Sign Language to Text: Using YOLOv3 with Background Subtraction and Edge Detection

The deaf–mute community faces a lot of problems when it comes to communication with people who do not know sign language. As the population of the community is rising, this problem is becoming more and more prevalent. ASL to text conversion will help the populace of this community by easing communication. In this method, we use You Only Look Once or YOLO version 3 to convert images of ASL letters into text. Due to the lack of a proper dataset, which is background and skin colour independent, we made our dataset. This dataset is independent of both the background and skin colour by using the MOG2 background subtraction algorithm. Experimental results show an accuracy of 90.43%.Kuriakose, Yohan Varghese Jangid, Mahesh
CitiSafe – Interactive Crime Reporting

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An Enhanced Visual Attention Siamese Network That Updates Template Features Online

Recently, Siamese trackers have attracted extensive attention because of their simplicity and low computational cost. However, for most Siamese trackers, only a frame of the video sequence is used as the template, and the template is not updated in inference process, which makes the tracking success rate inferior to the trackers that can update the template online. In the current study, we introduce an enhanced visual attention Siamese network (ESA-Siam). The method is based on a deep convolutional neural network, which integrates channel attention and spatial self-attention to improve the discriminative ability of the tracker for positive and negative samples. Channel attention reflects different targets according to the response value of different channels to achieve better target representation. Spatial self-attention captures the correlation between two arbitrary positions to help locate the target. At the same time, a template search attention module is designed to implicitly update the template features online, which can effectively improve the success rate of the tracker when the target is interfered by the background. The proposed ESA-Siam tracker shows superior performance compared with 18 existing state-of-the-art trackers on five benchmark datasets including OTB50, OTB100, VOT2016, VOT2018, and LaSOT.
Person Re-identification Algorithm Based on Spatial Attention Network

Person Re-identification (Re-ID) aims to solve the matching problem of the same pedestrian at a different time and in different places. Due to the cross-device condition, the appearance of different pedestrians may have a high degree of similarity, at this time, using the global features of pedestrians to match often cannot achieve good results. In order to solve these problems, we designed a Spatial Attention Network (SAN), which introduces attribute features as auxiliary information. Different from the previous approach of simply adding a branch of attribute binary classification network, our SAN is mainly divided into two connecting steps. First, we generate Attribute Attention Heat map (AAH) through Grad-CAM algorithm to accurately locate fine-grained attribute areas of pedestrians. Then, the Attribute Spatial Attention Module (ASAM) is constructed according to the AHH which is taken as the prior knowledge, and introduced into the Re-ID network to assist in the discrimination of the Re-ID task. In particular, our SAN network can integrate the local attribute information and global ID information of pedestrians, which has good adaptability. The test results on Market1501 and DukeMTMC-reID show that our SAN can achieve good results, which is obviously competitive compared with most Re-ID algorithms.
Improving Neural Network Performance by Embedding Expert Knowledge in The Form of Rules

This paper describes the process of capturing an expert’s knowledge in the form of human understandable rules and then inserting them into a self-organizing map neural network. The Dynamic Cell Structure (DCS) is a form of self-organizing map used for many purposes, including classification and prediction. The DCS is a topology-preserving network that starts with no pre-structure, but assumes a structure once trained. This paper explores applying the DCS to analyse a social science data set that contains some of the many factors that may influence the mortality rate of a region.
Uncertainty Aware Mobile Manipulator Platform Pose Planning Based on Capability Map

Mobile manipulators are increasingly applied to various scenarios. However, the mobile platform pose error caused by its positioning uncertainty has a great impact on the mobile manipulation task quality. A mobile platform pose where a mobile manipulator can achieve higher manipulability will increase the success rate of the mobile manipulation task especially in daily life applications. This paper presents an uncertainty aware pose planning method for mobile manipulator. First, a capability map is constructed to represent the manipulability distribution of the mobile manipulator and the manipulability expectation is used as the criteria in capability map querying to get the mobile platform pose with the highest expectation. Second, to speed up the expectation computing process, we introduce a filter, in which the mobile platform feasible poses are filtered according to the uncertainty distribution before expectation computing. Finally, experiments are carried out to show the applicability of the presented method.
A Hybrid Approach for Data-Based Models Using a Least-Squares Regression

An increased use of renewable energy could significantly reduce greenhouse gas emissions but is difficult to realize since most renewable energy sources underlie volatile availability. Making use of storage devices and scheduling consumers to times when energy is available allows to increase the amount of renewable energy that can be used. For this purpose, adequate models for forecasting the energy generated and consumed as well as for the behavior of storage devices are essential. Many data-based modeling approaches are computationally costly and therefore difficult to apply in real-world systems. Hence we present a computationally efficient modeling approach using a least-squares regression. Besides, we propose to use a hybrid model approach and evaluate it on real-world data at the examples of modeling the state of charge of a battery storage and the temperature inside a milk cooling tank. The experiments indicate that the hybrid approach leads to better forecasting results, especially for modeling more complicated behavior. Also, it is investigated if the behavior of the models is qualitatively realistic and we find that the battery model fulfills this requirement and is thus suitable for the application in a smart energy management system. Even though forecasts for the hybrid milk cooling model have even lower error values than the ones for the battery storage, further steps need to be taken to avoid undesired effects when using this model in such a sophisticated system.
Anomaly Detection for Discovering Performance Degradation in Cellular IoT Services

Connected and automated vehicles (CAVs) are envisioned to revolutionize the transportation industry, enabling autonomous processes and real-time exchange of information among vehicles and infrastructure. To safely navigate the roadways, CAVs rely on sensor readings and data from the surrounding vehicles. Hence, a fault or anomaly arising from the hardware, software, or the network can lead into devastating consequences regarding safety. This study investigates potential performance degradation caused by anomalies, by analyzing real-life vehicles’ sensory and network-related data. The aim is to utilize unsupervised learning for anomaly detection, with a goal to describe the cause and effect of the detected anomalies from a performance perspective. The results show around 93% F1-score when detecting anomalies imposed by the cellular network and the vehicle’s sensors. Moreover, with approximately 90% F1-score we can detect anomalous predictions from a deployed network-related ML model predicting cellular throughput and describe the root-causes behind the detected anomalies.
An Exhaustive Survey of Blockchain Applications with the Internet of Things (IoT) to Combat COVID-19 Spread

Coronavirus disease (COVID-19), triggered by the upsurge of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has emerged as a pandemic (widespread) in recent days which has made the life of people all over the globe standstill. Thus, in this strenuous situation, there is a need for applications that automate the process and complete with the minimum usage of human resources and well within the stipulated time along with security considerations. To mitigate the above-said challenges, ingenious technologies like Blockchain and Internet of Things (IoT) have come to light as promising solutions. The main intention of using IoT with Blockchain is to strengthen security and bring transparency in data collected from various external devices. Hence, this paper presents an exhaustive summary and review of the various Blockchain applications integrated with IoT to combat this COVID-19 situation. Taking into account a decent bunch of well-organized scholastic literature, we have broadly classified Blockchain-based IoT applications into four major segments which are smart healthcare, government, banking, and supply chain management to sustain in these difficult times. We shed light on a collective survey of COVID impact on abovementioned four segments, and then, we discuss measurable preventive actions that Blockchain with IOT can provide, and finally, we pull out challenges and future considerations that shed light on more research efforts in order to handle future coronavirus-like pandemic.
A method for behaviour recognition based on long short-term memory (LSTM) and splines

The paper describes a method for human behaviour recognition in surveillance applications. The method is based on the long short-term memory (LSTM), which is a form of recurrent neural network (RNN). LSTM has a memory function that can learn long-term dependencies by remembering short-term information for a long time. LSTM is therefore suitable for the events that are of interest in this paper. Except for the LSTM, the method also includes a simulation model for producing training and validation datasets. The simulation model describes a real environment with streets, buildings and squares and the motion patterns are represented with mathematical spline curves. The amount of training data are expanded further by creating different small variations of the spline curves. Output from the simulation model are matrices consisting of position, velocity and acceleration for a selected simulation time and sampling frequency. Before the data are used as input data to the LSTM a scaling is done so that the data patterns representing position, velocity and acceleration can contribute fully with useful information to the training process. The paper presents and discusses the method as well as results in the form of recognition accuracy of some simulated surveillance scenarios.
Exploration of graduation requirements in countries with high school credit systems

As the high school credit system is planned to be introduced to high schools nationwide in 2022, the related regulations and policies are being actively discussed and prepared. The high school credit system brings about many changes in terms of completion of the curriculum, evaluation, and graduation system, which are related to each other. In relation to graduation system, the review about the total number of days attended, the total number of graduation credits, the number of credits required for each subject, and the required course completion, graduation period is currently underway. The current high school graduation regulations are based on meeting the number of attendance days, so if you specify graduation only by attendance without considering credits or cumulative credits in the subject, you will not meet the basic concepts of the high school credit system. Therefore, in this paper, to draw out the implications and policy suggestions for the graduation system to be prepared when introducing the high school credit system, the author examined the research on the graduation requirements and graduation system in Korea in the current system, and then explored the graduation system of overseas countries. The graduation requirements in the United States, Canada, Finland, Germany, and Australia were extensively analyzed and explored. Many articles such as graduation components, attendance days, minimum or total credits, graduation exams, the level of administration, required course credits, grade level completion, and creativity creative experiential activity, should be specified in relevant laws or the national curriculum when establishing graduation requirements.
Incorporating Global Context into Multi-task Learning for Session-Based Recommendation

Session-based recommendation aims to predict the potential items that user may interact with next time from given anonymous sessions. However, existing session-based recommendation models mainly utilize the current given session without considering the global context information. The models that take collaborative neighbor information into account are vulnerable to noise, and their performance is not efficient and stable enough. To provide better prediction, in this paper, we propose a novel model, namely GCM-SR, which incorporates Global Context into Multi-task learning for Session-based Recommendation. Rather than directly integrating the global context information with the session-level item transition information, GCM-SR regards the global context as implicit type information and integrates it in the form of an auxiliary task to enhance recommendation performance. Then, local learning task and global learning task are joined through adjustable weights to complete prediction and recommendation. Experiments on three public datasets demonstrate the superiority of GCM-SR over the state-of-the-art models. The results may give suggestion for better performance in session-based recommendation systems.
Fourier-Reflexive Partitions Induced by Poset Metric

Let <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathrm{H}=\prod\nolimits_{i\in\Omega}H_{i}$</tex> be the cartesian product of finite abelian groups <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$H_{i}$</tex> indexed by a finite set <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega$</tex> . Any partition of H gives rise to a dual partition of its character group <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\hat{\mathrm{H}}$</tex> . A given poset (i.e., partially ordered set) P on <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Omega$</tex> gives rise to the corresponding poset metric on H, which further leads to a partition <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Gamma$</tex> of H. We prove that if <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\Gamma$</tex> is Fourier-reflexive, then its dual partition <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\hat{\Gamma}$</tex> coincides with the partition of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\hat{\mathrm{H}}$</tex> induced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\overline{\mathrm{P}}$</tex> , the dual poset of P, and moreover, P is necessarily hierarchical. This result establishes a conjecture proposed by Heide Gluesing-Luerssen in [4]. We also show that with some other assumptions, <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\hat{\Gamma}$</tex> is finer than the partition of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\hat{\mathrm{H}}$</tex> induced by <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\overline{\mathrm{P}}$</tex> . We prove these results by relating the partitions with certain family of polynomials, whose basic properties are studied in a slightly more general setting.
Modulation Recognition of Underwater Acoustic Communication Signals Based on Deep Heterogeneous Network

The complicated underwater environment and its variable characteristics bring certain challenges to the modulation recognition of acoustic communication signals. Traditional methods for the task are usually based on manually extracted features, which require sufficient prior knowledge and artificial cost. In this paper, considering the rapid development of machine learning technology, the original underwater acoustic signal data are compressed by PCA technique, so as to reduce the data dimension and suppress the noise interference. On the basis, a deep heterogeneous network combining hybrid dilated convolutional networks and Long-Short Term Memory network is built to automatically capture the hidden features of data series to achieve the modulation recognition of 4 underwater acoustic communication signals recognition, including OOK, 2FSK, 2PSK and QPSK. Under different SNRs, the simulation experimental results show that the proposed network is valid and robust to identify 4 modulation modes. In the actual experiment, recognition accuracy of 91.171% confirms the effectiveness of the proposed network for modulation classification.
K′ times k-means logistic regression algorithm for imbalanced classification

Because of its simplicity and high efficiency, logistic regression algorithm has been widely applied in the field of data mining. However, logistic regression classifiers always show poor performance when the categories are imbalanced. In this paper, a new algorithm named K′KLR (k′ times k-means logistic regression) is proposed to deal with the imbalanced classification problem. Compared to other traditional methods, the effectiveness of the proposed algorithm is verified through experiments based on simulated data and real data.