Reviewerly/exHarmony huggingface dataset:
Deep Learning-Based Traffic Behavior Analysis under Multiple Camera Environment

In a video surveillance system, tracking multiple moving objects using a single camera feed is having numerous challenges. A multi-camera system increases the output image quality in both overlapping and non-overlapping environment. Traffic behavior analysis is an intensified demand in a recent topic of research. Due to increasing traffic in intercity roads, interstate, and national highways. Automated traffic visual surveillance applications with the multi-camera are a topic of research in computer vision. This paper, present a multi-camera system study for the overlapping area of the road for traffic analysis in three sections. The second section represents the thorough literature survey on the multi-camera system. Here, the third section is our proposed system using a dual-camera experimental setup with their coordination. A deep neural network is used in the experiments for traffic behavior analysis. The emphasis of this paper is on the physical arrangement of the multi-camera system, calibration, and advantages- disadvantages. On a conclusion note, future development and advancement in traffic analysis using a multi-camera system is discussed.
Benchmarking the utility of maps of dynamics for human-aware motion planning

Robots operating with humans in highly dynamic environments need not only react to moving persons and objects but also to anticipate and adhere to patterns of motion of dynamic agents in their environment. Currently, robotic systems use information about dynamics locally, through tracking and predicting motion within their direct perceptual range. This limits robots to reactive response to observed motion and to short-term predictions in their immediate vicinity. In this paper, we explore how maps of dynamics (MoDs) that provide information about motion patterns outside of the direct perceptual range of the robot can be used in motion planning to improve the behaviour of a robot in a dynamic environment. We formulate cost functions for four MoD representations to be used in any optimizing motion planning framework. Further, to evaluate the performance gain through using MoDs in motion planning, we design objective metrics, and we introduce a simulation framework for rapid benchmarking. We find that planners that utilize MoDs waste less time waiting for pedestrians, compared to planners that use geometric information alone. In particular, planners utilizing both intensity (proportion of observations at a grid cell where a dynamic entity was detected) and direction information have better task execution efficiency.
Comprehensive Analysis of Various Big Data Classification Techniques: A Challenging Overview

Data over the internet has been increasing everyday, and automatic mining of essential information from an enormous amount of data has become a challenging task today for an organisation with a huge dataset. In recent years, the prominent technology in the domain of Information Technology (IT) is big data, which is unstructured data that solves the computational complexity of classical database systems. The data is fast and big and typically derived from multiple and independent sources. The three main challenges are data accessing, semantics, and domain knowledge for various big data utilisations and complexities raised by big data volumes. One of the major limitations is the classification of big data. This paper introduces well-defined classification methodologies employed for big data classification. This paper reviews 50 research papers based on classification methods of big data, and such methodologies are primarily categorised into six different categories, namely K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Fuzzy-based method, Bayesian-based method, Random Forest, and Decision Tree. In addition, detailed analysis and discussion are carried out by considering classification techniques, dataset utilised, evaluation metrics, semantic similarity measures, and publication year. In addition, research gaps and issues for several traditional big data classification techniques are explained to expand investigators’ works to provide effective big data management.
Re-LSTM: A long short-term memory network text similarity algorithm based on weighted word embedding

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Bodily-awareness-in-reflection: Advancing the epistemological foundation of post-simulation debriefing

Reflection is generally considered to be important for learning from simulation-based training in professional and vocational education.The mainstream conceptualization of reflection is argued to rest on a dualistic ground separating the mind from the body.Drawing on phenomenological analyses of bodily awareness and an ethnographic case study of maritime safety training we show how and why students' embodied experiences and subjectivity play a foundational role in reflection.We develop and illustrate the notion of bodily-awareness-in-reflection which captures a mode of reflection wherein non-conceptual pre-reflective bodily experiences are integrated with conceptual thought.In this way, we aim to enrich and complement prior work on simulation-based training and embodied professional learning by providing an enactivist epistemological foundation of reflection in debriefing, highlighting awareness of the lived body as a necessary ground of reflective self-knowledge.
Novel feature extraction method of pipeline signals based on multi-scale dispersion entropy partial mean of multi-modal component

This paper considers the problem of feature extraction of pipeline acoustic signals under different working conditions. A novel method is proposed based on multi-scale dispersion entropy partial mean (MDEPM) of multi-modal component to extract features of pipeline signals. First, variational mode decomposition (VMD) algorithm is applied to decompose the acoustic signals to obtain several mode components. Then, Kolmogorov–Smirnov distance (KSD) is introduced as the index to measure the correlation between each mode component and the original signal, and the mode component with a smaller KSD is selected as the feature component. Finally, the MDEPM of the feature component is calculated so as to form the feature vector, which realizes signal feature extraction. In experiment, 13 different condition signals collected are divided into three categories, the experimental results show that the proposed method could extract the characteristics of the different pipeline acoustic signals. Furthermore, the extracted features could be accurately identified and classified by extreme learning machine (ELM) under different working conditions, and then through comparing with other methods, the feasibility and superiority of the proposed method are verified.
Predicting the citation count and CiteScore of journals one year in advance

Prediction of the future performance of academic journals is a task that can benefit a variety of stakeholders including editorial staff, publishers, indexing services, researchers, university administrators and granting agencies. Using historical data on journal performance, this can be framed as a machine learning regression problem. In this work, we study two such regression tasks: 1) prediction of the number of citations a journal will receive during the next calendar year, and 2) prediction of the Elsevier CiteScore a journal will be assigned for the next calendar year. To address these tasks, we first create a dataset of historical bibliometric data for journals indexed in Scopus. We propose the use of neural network models trained on our dataset to predict the future performance of journals. To this end, we perform feature selection and model configuration for a Multi-Layer Perceptron and a Long Short-Term Memory. Through experimental comparisons to heuristic prediction baselines and classical machine learning models, we demonstrate superior performance in our proposed models for the prediction of future citation and CiteScore values.
The geometry of one-weight codes in the sum-rank metric

We provide a geometric characterization of k-dimensional Fqm-linear sum-rank metric codes as tuples of Fq-subspaces of Fqmk. We then use this characterization to study one-weight codes in the sum-rank metric. This leads us to extend the family of linearized Reed-Solomon codes in order to obtain a doubly-extended version of them. We prove that these codes are still maximum sum-rank distance (MSRD) codes and, when k=2, they are one-weight, as in the Hamming-metric case. We then focus on constant rank-profile codes in the sum-rank metric, which are a special family of one weight-codes, and derive constraints on their parameters with the aid of an associated Hamming-metric code. Furthermore, we introduce the n-simplex codes in the sum-rank metric, which are obtained as the orbit of a Singer subgroup of GL(k,qm). They turn out to be constant rank-profile – and hence one-weight – and generalize the simplex codes in both the Hamming and the rank metric. Finally, we focus on 2-dimensional one-weight codes, deriving constraints on the parameters of those which are also MSRD, and we find a new construction of one-weight MSRD codes when q=2.
Video representation learning for temporal action detection using global-local attention

• Obtaining representations with different properties for the two sub-tasks (action classification and action localization) of temporal action detection leads to better performance. • Attention maps based on feature similarity can enhance intra-class homogeneity and inter-class heterogeneity of video representation for action classification sub-task. • Adaptive receptive fields based on actions facilitate precise action localization. Video representation is of significant importance for temporal action detection. The two sub-tasks of temporal action detection, i.e., action classification and action localization, have different requirements for video representation. Specifically, action classification requires video representations to be highly discriminative, so that action features and background features are as dissimilar as possible. For action localization, it is crucial to obtain information about the action itself and the surrounding context for accurate prediction of action boundaries. However, the previous methods failed to extract the optimal representations for the two sub-tasks, whose representations for both sub-tasks are obtained in a similar way. In this paper, a Global-Local Attention (GLA) mechanism is proposed to produce a more powerful video representation for temporal action detection without introducing additional parameters. The global attention mechanism predicts each action category by integrating features in the entire video that are similar to the action while suppressing other features, thus enhancing the discriminability of video representation during the training process. The local attention mechanism uses a Gaussian weighting function to integrate each action and its surrounding contextual information, thereby enabling precise localization of the action. The effectiveness of GLA is demonstrated on THUMOS’14 and ActivityNet-1.3 with a simple one-stage action detection network, achieving state-of-the-art performance among the methods using only RGB images as input. The inference speed of the proposed model reaches 1373 FPS on a single Nvidia Titan Xp GPU. The generalizability of GLA to other detection architectures is verified using R-C3D and Decouple-SSAD, both of which achieve consistent improvements. The experimental results demonstrate that designing representations with different properties for the two sub-tasks leads to better performance for temporal action detection compared to the representations obtained in a similar way.
Leveraging Synthetic Data for DNN-Based Visual Analysis of Passenger Seats

Deep neural network (DNN)-based vision systems could improve passenger transportation safety by automating processes such as verifying the correct positioning of luggage, seat occupancy, etc. Abundant and well-distributed data are essential to make DNNs learn appropriate pattern recognition features and have enough generalization ability. The use of synthetic data can reduce the effort of generating varied and annotated data. However, synthetic data usually present a domain gap with real-world samples, that can be reduced with domain adaptation techniques. This paper proposes a methodology to build simulated environments to generate balanced and varied synthetic data and avoid including redundant samples to train classification DNNs for passenger seat analysis. We show a practical implementation for detecting whether luggage is correctly placed or not in an aircraft cabin. Experimental results show the contribution of the synthetic samples and the importance of correctly discarding redundant data.
Sparse Channel Pruning and Assistant Distillation for Faster Aerial Object Detection

In recent years, object detectors based on convolutional neural networks have been widely used on remote sensing images. However, the improvement of their detection performance depends on a deeper convolution layer and a complex convolution structure, resulting in a significant increase in the storage space and computational complexity. Although previous works have designed a variety of new lightweight convolution and compression algorithms, these works often require complex manual design and cause the detector to be greatly modified, which makes it difficult to directly apply the algorithms to different detectors and general hardware. Therefore, this paper proposes an iterative pruning framework based on assistant distillation. Specifically, a structured sparse pruning strategy for detectors is proposed. By taking the channel scaling factor as a representation of the weight importance, the channels of the network are pruned and the detector is greatly slimmed. Then, a teacher assistant distillation model is proposed to recover the network performance after compression. The intermediate models retained in the pruning process are used as assistant models. By way of the teachers distilling the assistants and the assistants distilling the students, the students’ underfitting caused by the difference in capacity between teachers and students is eliminated, thus effectively restoring the network performance. By using this compression framework, we can greatly compress the network without changing the network structure and can obtain the support of any hardware platform and deep learning library. Extensive experiments show that compared with existing detection networks, our method can achieve an effective balance between speed and accuracy on three commonly used remote sensing target datasets (i.e., NWPU VHR-10, RSOD, and DOTA).
Assessing Human Mobility by Constructing a Skeletal Database and Augmenting it Using a Generative Adversarial Network (GAN) Simulator

This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.
COVID-19 sentiment analysis using college subreddit data

The COVID-19 pandemic has affected our society and human well-being in various ways. In this study, we investigate how the pandemic has influenced people's emotions and psychological states compared to a pre-pandemic period using real-world data from social media.We collected Reddit social media data from 2019 (pre-pandemic) and 2020 (pandemic) from the subreddits communities associated with eight universities. We applied the pre-trained Robustly Optimized BERT pre-training approach (RoBERTa) to learn text embedding from the Reddit messages, and leveraged the relational information among posted messages to train a graph attention network (GAT) for sentiment classification. Finally, we applied model stacking to combine the prediction probabilities from RoBERTa and GAT to yield the final classification on sentiment. With the model-predicted sentiment labels on the collected data, we used a generalized linear mixed-effects model to estimate the effects of pandemic and in-person teaching during the pandemic on sentiment.The results suggest that the odds of negative sentiments in 2020 (pandemic) were 25.7% higher than the odds in 2019 (pre-pandemic) with a p-value &lt; 0.001; and the odds of negative sentiments associated in-person learning were 48.3% higher than with remote learning in 2020 with a p-value of 0.029.Our study results are consistent with the findings in the literature on the negative impacts of the pandemic on people's emotions and psychological states. Our study contributes to the growing real-world evidence on the various negative impacts of the pandemic on our society; it also provides a good example of using both ML techniques and statistical modeling and inference to make better use of real-world data.
Approximating Shading Ratio Using the Total-Sky Imaging System: An Application for Photovoltaic Systems

In recent years, a determined shading ratio of photovoltaic (PV) systems has been broadly reviewed and explained. Observing the shading ratio of PV systems allows us to navigate for PV faults and helps to recognize possible degradation mechanisms. Therefore, this work introduces a novel approximation shading ratio technique using an all-sky imaging system. The proposed solution has the following structure: (i) we determined four all-sky imagers for a region of 25 km2, (ii) computed the cloud images using our new proposed model, called color-adjusted (CA), (iii) computed the shading ratio, and (iv) estimated the global horizontal irradiance (GHI) and consequently, obtained the predicted output power of the PV system. The estimation of the GHI was empirically compared with captured data from two different weather stations; we found that the average accuracy of the proposed technique was within a maximum ±12.7% error rate. In addition, the PV output power approximation accuracy was as high as 97.5% when the shading was zero and reduced to the lowest value of 83% when overcasting conditions affected the examined PV system.
Development and Study of an Encryption Algorithm

A new symmetric block cipher algorithm called AL02 has been developed. The algorithm scheme provides five-round encryption of 128-bit blocks, while the data size at the input and output of the S-box is 8 bits. The main transformation is the F transformation. The difference between the proposed algorithm and the classical scheme is that the F transformation provides the maximum possible dependence of the output vector bits on the input bits and is based on “modulo 2 addition” and a substitution S-box. To assess the strength of the AL02 algorithm, it was programmatically implemented in the C programming language. During the analysis, the cryptographic properties of the developed encryption algorithm were tested. The algorithm was tested for statistical security. For an experimental assessment, in order to ensure that the ciphertext is not inferior to a random sequence in its properties, the well-known sets of statistical tests by NIST (National Institute of Standards and Technology) and Donald Knuth were used. The property of the avalanche effect was also checked. The strength was evaluated using the methods of differential and linear cryptanalysis.
Enhancing Space Security Utilizing the Blockchain: Current Status and Future Directions

The Blockchain concept is often mostly associated with Bitcoin and monetary transactions, however, the technology has enormous potential for several industries including the space sector. Cybersecurity enhancement plays a detrimental role in critical core operations inside the space functional flow: from the supply chain, satellite orbital tracking, and digital communications efficacy. The increasing adaption of connected highly advanced technologies exposes satellites, aeronautical drones, space vehicles, and ground stations to new types of cybersecurity risks. Hacking, cyber threats, as well as cyber-crimes in space are new trends. Such industrial ecosystems can benefit from Blockchain & distributed ledger technologies (DLT) that make it possible to intelligently decentralize governance. This work attempts to further demystify the linkage between space and the Blockchain, address the ledger's potential to provide added-on cybersecurity to the critical space core functionalities, via a ready-prototyped DLT/IOTA secure framework (SWIoTA), and finally, illuminate the roles of DLT ledger to secure networks of satellites orbits, as well as space tokenization concept.
Efficient Method of Finding the Best Time Interval for Predicting Short Term Solar Radiation Using CNN-LSTM Model

Even though the application of solar energy for electric power production is rapidly growing throughout the world, its unpredictability continues to provide significant difficulties. The primary source of this issue is the fluctuation of the solar radiative power, which the Photovoltaic (PV) cells convert into electrical energy at the power plants. In determining the best time interval or horizon for solar irradiance forecasting, the CNN-LSTM hybrid model was used. The input data consisted of historical solar irradiance obtained at five different time intervals over two years period. The dataset was created using historical meteorological data for Cape Town for two years. Eighty percent of the whole dataset was used to train the model for up to 1,000 epochs. The metric deployed to assess the model’s performance was Root Mean Squared Error (RMSE). Results from this experiment were compared with those from the Support Vector Regression (SVR) model that was fitted independently using a similar volume of data. From the performance metrics analyzed, the CNN-LSTM achieved better results than the SVR model. It recorded an RMSE of 6.2 percent using training data collected at 5-minute intervals. This result was best when contrasted with others obtained when the models were trained with data obtained from other different horizons. Adopting the data produced by the CNN-LSTM hybrid model at the five-minute horizon in Cape Town is suggested to improve control over the issues caused by fluctuating solar radiative power on the power system smart grid.