Reviewerly/exHarmony huggingface dataset:
Machine Learning-based Human Activity Recognition using Neighbourhood Component Analysis

Human activity recognition emerges as a rising field of investigation in achieving brilliant medicinal services observing and benefits for ambient assisted living (AAL). By using the signs amassed from different sensors inbuilt in cell phones, the HAR can perceive the exercises of subjects. For our work, seven unique kinds of exercises (online available dataset) are utilized and Neighborhood Component Analysis (NCA) is utilized to choose the significant features from the huge dataset. In this way, the computational cost is reduced. The grouping models, for example, Random Forest and K-Nearest Neighbor are utilized to distinguish the seven distinctive human exercises. The activities are classified using KNN and Random Forest classifiers with different neighbors and results are tabulated.
A Roadmap on Handwritten Gujarati Digit Recognition using Machine Learning

One of the major difficulties in the area of pattern recognition and image processing is handwritten character recognition (HCR). For human, this seems to be very easy but for a machine, the task of recognizing handwritings is tedious. In case of machine, first the input needs to be scanned from file, image and real-time device like personal computers, digitizers and tablets. After that, the scanned input is translated in digital text in Handwritten Character Recognition process. There are two ways this can be done: online and offline. In online method, the input is taken at runtime whereas in offline method, the input is a scanned file. The mechanism where the machine understands an image of a handwritten script automatically is known as Offline handwritten character recognition. Various applications of HCR are processing of banks, mail sorting and reading of documents etc. As per the survey carried out, it was seen that the Offline handwriting is relatively challenging, to identify, as different people have different handwriting. As machine learning is a buzz word nowadays and most of the real-time applications are solved easily using the models of machine learning, hence our paper focuses on comparing various techniques of machine learning used to recognize the Gujarati handwriting digits. In Gujarat State, India, Gujarati script used by people to write the Gujarati language.
Adaptive RF Fingerprint Decomposition in Micro UAV Detection based on Machine Learning

Radio frequency (RF) signal classification has significantly been used for detecting and identifying the features of unknown unmanned aerial vehicles (UAVs). This paper proposes a method using empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) on extracting the communication channel characteristics of intruding UAVs. The decomposed intrinsic mode functions (IMFs) except noise components are selected for RF signal pattern recognition based on machine learning (ML). The classification results show that the denoising effects introduced by EMD and EEMD could both fit in improving the detection accuracy with different features of RF communication channel, especially on identifying time-varying RF signal sources.
Automatic Class Decomposition using Clustering

This paper presents an automated technique for architectural decomposition of classes using K-Means clustering. The goal is to help designers in grouping related classes into subsystems or packages. The proposed technique utilizes the K-Means clustering algorithm to cluster a set of java classes into disjoint groups. The technique analyzes source code of classes to represent classes as sets of identifiers. Then, the K-Means clustering technique is applied on these identifiers. The proposed technique has been evaluated on a number of packages from an open source project. Results showed that the proposed technique can achieve good results when the system architecture is composed of packages with large number of classes.
A CNN Based Deep Learning Approach for Leukocytes Classification in Peripheral Blood from Microscopic Smear Blood Images

As a cardinal sign of the pathological condition of human health, WBC also known as leukocytes has prominently regulated a functional role in the body’s immune system. So, the classification of white blood cells has been kept on great precedence in the diagnosis of hematological diseases such as leukemia or any kind of blood cancers even AIDS so. But the evaluation process is quite complicated and painstaking as leukocytes include a series of variety within it. Following the drawbacks, deep learning can offer much refinement in the classification manner of leukocytes, especially, the convolutional neural network, i.e., CNN, an advanced and sophisticated technology that has been conceded widespread as a compatible approach regarding the visual image analysis. Thus considering the pledges, a new approach has been introduced in this paper based on a precisely established CNN model with an adjuration of outrun the traditional approaches proposed before that includes multiple complicated steps such as preprocessing, segmentation, feature extraction, and so on in classifying four types of white blood cells. The model is established on a feasible study and a series of experiments conducted on large-scale training sets of 9957 annotated blood smear images of WBCs (neutrophils, eosinophils, monocytes, and lymphocytes) to perceive a hierarchical manner of learning and understanding automatically. And then, the detection model is evaluated on test sets of 2487 annotated blood smear images of WBCs. This diligent research results in salient recognition performance in classifying four types of WBCs with an accuracy of 98% on average. So, our proposed customized model can be used in the real-life healthcare sector for identifying diseases that also reduce the disease detection cost.
Penalized K-Means Algorithms for Finding the Number of Clusters

In many applications we want to find the number of clusters in a dataset. A common approach is to use a penalized k-means algorithm with an additive penalty term linear in the number of clusters. Obviously, the number of discovered clusters depends critically on the value of the coefficient of the penalty term, and an open problem is estimating the value of the coefficient in a principled manner. In this paper, we derive rigorous bounds for the coefficient of the additive penalty in k-means for ideal clusters. Although in practice clusters typically deviate from the ideal assumption, the ideal case serves as a useful guideline. Furthermore, we investigate k-means with multiplicative penalty, which generally produces a more reliable signature, compared to additive penalty, for the correct number of clusters in cases where the ideal cluster assumption holds. We also empirically investigate certain types of deviations from ideal cluster assumption. In such cases both types of penalties may suggest multiple, ambiguous solutions. We present a consensus-based approach to resolving these ambiguous solutions by combining the results of additive and multiplicative penalties.
Generalizing across auxiliary, statistical, and inferential assumptions

Abstract There is a long history of discussion about the ability of researchers to generalize their findings. But findings are not the only entity that researchers can attempt to generalize. Scientists have theories, empirical hypotheses, and statistical hypotheses too. The extent to which scientists can generalize these is an open issue. As a prerequisite to proposals about generalizing theories, empirical hypotheses, and statistical hypotheses; they must be distinguished from each other. Another prerequisite is to specify, across what, the researcher wishes to generalize. I show that theories may or may not generalize across auxiliary assumptions, empirical hypotheses may or may not generalize across statistical assumptions, and statistical hypotheses may or may not generalize across inferential assumptions. The reasoning to be presented, pertaining to generalization, militates against the typical practice of using p ‐values to draw conclusions about hypotheses.
Deep Hashing Using N-pair Loss for Image Retrieval

Deep hashing algorithm is one of the most effective techniques for the approximate nearest neighbor search for large-scale image retrieval. Existing deep hash algorithms are based on paired labels and triple ordering loss, they usually only interact with one negative class, and the convergence speed is too slow. In this paper, we propose a novel deep hashing algorithm called N-pair loss deep hashing (NPLDH), which optimization based on the N-pair loss function can help deep hash models to train more effectively. Experimental results show that our NPLDH algorithm achieves higher performance in image retrieval algorithms on the CIFAR-10 and NUS-WIDE datasets.
Vision-Based Control of an Industrial Vehicle in Unstructured Environments

Most of the previously proposed vehicle controllers are developed with the no-skid assumption, also presume that full states of the vehicle are available, which are usually violated for industrial tractors. This article dedicates to building a practical vision-based antiskid tracking control framework for industrial tractors. For that, we first present a novel nonlinear visual–inertial estimator (VIE) to estimate the real-time position and velocity of the tractor in unstructured environments (GPS-denied, no prior feature maps, etc.). Subsequently, VIE-based kinematic and dynamic controllers are designed for trajectory tracking. The proposed kinematic controller is easy to implement. It works well even if the vehicle is towing payloads since the sideslip angle can be reactively estimated and compensated. Besides, by fully considering vehicle dynamics, the VIE-based dynamic controller can be compatible with occasions that require higher control performance. It is proved by Lyapunov theory that the asymptotic stability of the closed-loop estimation-control system is guaranteed. Extensive full-scale experiments on an autonomous tractor are implemented to show the validity of the VIE-based control system in unstructured environments.
A study of 100kWp PV Plant Output Power Forecasting: A case study

Nowadays solar energy penetration in the local grids is increasing more; this may result in a reduction in reliability, so it requires the future power generation forecasts to improve the grid reliability and to make the grid smarter. This can be possible with Data Analysis; here large volume of time series data are analyzed in python using AI/ML techniques. Tis paper has implemented a DNN (Deep Neural Network) technique using LSTM networks for forecasting the PV Power Generation. For this, the previously recorded generation data of 100kWp Solar Power Plant for past two years (720 day wise data samples) are considered and also tested on 220 hourly data samples for predicting monthly and day ahead power generation. This time-series data processed through the LSTM Networks for forecasting monthly and hourly PV generation. These results are compared with the other forecasting models viz. ARIMA, SARIMA, RNN and fbProphet and observed that LSTM and Prophet are giving better forecasting results. Root Mean Square Error (RMSE) value and the plant’s Capacity Utilization Factor (CUF) was calculated. This study shows that the LSTM networks are more accurate for forecasting the time series data.
A 252 Spins Scalable CMOS Ising Chip Featuring Sparse and Reconfigurable Spin Interconnects for Combinatorial Optimization Problems

Annealing processors [1] -[4] based on the Ising model and quantum or simulated annealing mechanism recently have gained significant attention due to the unique capabilities of solving problems based on the convergence behavior in the stochastic spin interactions. Spin is a neuron-like binary (i.e. -1 or +1) unit of annealing processors, and a network of such spins solves combinatorial optimization problems by minimizing its Hamiltonian (or energy of the system) to the ground state. Although prior works [1] -[3] have demonstrated the annealing processors' capability to solve problems that are ill-suited to classical computers, their applications are limited due to simple and fixed spin networks, such as lattice, Chimera [1], and King's graph -[3]. As shown in Fig. 1 (top-left), vertices (i.e. spins) in simple graphs interact with neighbors via local interconnects, and the number of neighbors is typically limited to 4-to-8. Fig. 1 (top-center) shows a recent work [4] implementing a complete graph with fully-connected spins based on the near-memory computing architecture. However, it requires a significant energy overhead and results in scalability challenges due to the excessive hardware complexity. The number of interconnects increases quadratically in proportion to the number of spins.
Does Mixed Reality Have a Cassandra Complex?

OPINION article Front. Virtual Real., 17 May 2021Sec. Virtual Reality and Human Behaviour https://doi.org/10.3389/frvir.2021.673547
Identifying Differentiating Factors for Cyberbullying in Vine and Instagram

A multitude of online social networks (OSNs) of varying types has been introduced in the past decade. Because of their enormous popularity and constant availability, the threat of cyberbullying launched via these OSNs has reached an unprecedented level. Victims of cyberbullying are now more vulnerable than ever before to the predators, perpetrators, and stalkers. In this work, we perform a detailed analysis of user postings on Vine and Instagram social networks by making use of two labeled datasets. These postings include threads of media posts and user comments that were labeled for being cyberbullying instances or not. Our analysis has revealed several important differentiating factors between cyberbullying and non-cyberbullying instances in these social networks. In particular, cyberbullying and non-cyberbullying instances differ in (i) the number of unique negative commenters, (ii) temporal distribution of positive and negative sentiment comments, and (iii) textual content of media captions and subsequent comments. The results of these analyses can be used to build highly accurate classifiers for identifying cyberbullying instances.
An approach for cancer classification using optimization driven deep learning

Abstract The normal and cancer cell tissues exhibit different gene expressions. Therefore, gene expression data are the effective source for cancer classification, in which the usage of the original gene expression data is challenging due to their high dimension and small size of the data samples. This article proposes a fractional biogeography‐based optimization‐based deep convolutional neural network (FBBO‐based deep CNN) for cancer classification. The developed FBBO is the integration of the fractional calculus (FC) in the biogeography‐based optimization (BBO), which aims at determining the optimal weights for tuning the deep CNN. Initially, the gene expression data is pre‐processed and subjected to dimensional reduction using the probabilistic principal component analysis (PPCA). The selected features are used for cancer classification enabling a high degree of robustness and accuracy. The experimental analysis using the Colon dataset and Leukemia dataset reveals that the proposed classifier acquired maximal accuracy, sensitivity, specificity, precision, and F‐Measure of 0.98.
LMD-TShip<sup>⋆</sup>: Vision Based Large-Scale Maritime Ship Tracking Benchmark for Autonomous Navigation Applications

Accurate ship tracking is very important for the security of maritime activities, especially the raising requirements of autonomous navigation applications, e.g., autonomous surface vehicles (ASVs).Unlike deep-learning-based object-tracking methods are prevailing in autonomous driving because of good environmental robustness and high tracking accuracy, few deep-tracking models can be found for maritime ships.The main reason for that is the lack of qualified ship datasets, especially datasets with ship-based perspectives.Therefore, a large-scale, high-definition dataset for ship tracking, LMD-TShip (Large Maritime Dataset), is provided in this paper.In this dataset, five types of ships are included, from cargo ships, fishing ships, passenger ships, and speed boats to unmanned ships.Specifically, LMD-TShip consists of 40,240 frames in 191 videos, each of which is carefully and manually annotated with bounding boxes in YOLO format.Moreover, 13 attributes are used to label videos, e.g., scale variation (SV), occlusion (OCC), basically covering tracking challenges of maritime ship tracking.Next, a detailed analysis is carried out to demonstrate the characteristics of LMD-TShip.Finally, experiments with five baseline short-term tracking models on the dataset are performed, e.g., ECO, SiamRPN++, and the experimental results demonstrate its good evaluation ability, which will provide effective means for training and testing tracking models related to maritime ships.
Towards the Management and Mitigation of Motion Sickness – An Update to the Field

Almost everyone can experience motion sickness and one third of the population are highly susceptible. With growing development and popularity of technologies such as self-driving cars, simulators and virtual reality (VR), motion sickness management will be more of a consideration in the future than ever before. People who are susceptible to motion sickness may not gain the full benefits of self-driving cars (e.g., increased productivity), have access to vocations involving significant simulator-based training (e.g., airplane pilots), or have access to the increased opportunities that VR headsets may bring (e.g., vocational training or job roles involving VR). Further, with demographic variance within susceptibility to motion sickness, it is known some demographic groups are far more susceptible to motion sickness than others (e.g., females vs. Males), which further identifies an inclusivity aspect to these technologies. This report evidences the strong motivation towards the mitigation of motion sickness and discusses the associated benefits. Working towards the objective of enhanced motion sickness management, this paper presents a new model to detail the onset of motion sickness syndrome and discusses the causal relationship between sensory conflict and the physiological and psychological effects of motion sickness. In doing so we identify within the existing literature many methods towards the management (both prevention and mitigation) of motion sickness and provide a direction for further study.
Symmetric Sub-graph Spatio-Temporal Graph Convolution and its application in Complex Activity Recognition

Understanding complex hand actions, such as assembly tasks or kitchen activities, from hand skeleton data is an important yet challenging task. In this paper, we analyze hand skeleton-based complex activities by modeling dynamic hand skeletons through a spatiotemporal graph convolutional neural network (ST-GCN). This model jointly learns and extracts Spatio-temporal features for activity recognition. Our proposed technique, Symmetric Sub-graph spatio-temporal graph convolutional neural network (S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -ST-GCN), exploits the symmetric nature of hand graphs to decompose them into smaller sub-graphs, which allow us to build a separate temporal model for the relative motion of the fingers. This subgraph approach can be implemented efficiently by preprocessing input data using a Haar unit based orthogonal matrix. Then, in addition to spatial filters, separate temporal filters can be learned for each sub-graph. We evaluate the performance of the proposed method on the First-Person Hand Action dataset. While the proposed method shows comparable performance with the state of the art methods in train:test=1:1 setting, it achieves this with greater stability. Furthermore, we demonstrate significant performance improvement in comparison to state of the art methods in the cross-person setting, where the model did not come across a test subject's data while learning. S <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> -ST-GCN also shows superior performance than a finger-based decomposition of the hand graph where no preprocessing is applied.
Underproduction: An Approach for Measuring Risk in Open Source Software

The widespread adoption of Free/Libre and Open Source Software (FLOSS) means that the ongoing maintenance of many widely used software components relies on the collaborative effort of volunteers who set their own priorities and choose their own tasks. We argue that this has created a new form of risk that we call ‘underproduction' which occurs when the supply of software engineering labor becomes out of alignment with the demand of people who rely on the software produced. We present a conceptual framework for identifying relative underproduction in software as well as a statistical method for applying our framework to a comprehensive dataset from the Debian GNU/Linux distribution that includes 21,902 source packages and the full history of 461,656 bugs. We draw on this application to present two experiments: (1) a demonstration of how our technique can be used to identify at-risk software packages in a large FLOSS repository and (2) a validation of these results using an alternate indicator of package risk. Our analysis demonstrates both the utility of our approach and reveals the existence of widespread underproduction in a range of widely-installed software components in Debian.
Value-based Algorithms Optimization with Discounted Multiple-step Learning Method in Deep Reinforcement Learning

Value-based algorithms have been demonstrated on a range of deep reinforcement learning tasks. However, value-based algorithms suffer from challenges in the aspect of stability, overestimate and convergence, which limit severely the application of such algorithms in real-word environment. In n-step learning method, truncated N-step return was used as a part of multiple-step targets to make faster learning, and improve the defect of such algorithms, but it is still far away to practical application. In this paper, we proposed a straightforward optimal method - Discount Multiple-steps Learning Method (DMLM) to improve the performance of value-based algorithms by giving a discount factor to truncated N-step return which shows better results in our experiments. In this method, regard the discounted truncated N-step return rather than accumulated discount reward as the important part of target network when computing the TD-error as a loss function of evaluate network. In the experiment part, we perform experiments compare to value-based algorithms without this method, and prove this method can make more accurate predict of value function, thereby outperform other optimal methods in terms of stability, overestimate and convergence.
The verification system for interface intelligent perception of human-computer interaction

The verification system for interface intelligent perception of human-computer interaction for the device with “intelligent perception” and “natural interaction” has been explored in this paper, aiming to discover the specific application of intelligent perception, improve the intelligent level of equipment, provide more natural human-computer interaction for operators so as to achieve the human-computer coordination in a deeper level and enhance the immersion of the operators.