Reviewerly/exHarmony huggingface dataset:
Fall Detection Using Multi-Property Spatiotemporal Autoencoders in Maritime Environments

Man overboard is an emergency in which fast and efficient detection of the critical event is the key factor for the recovery of the victim. Its severity urges the utilization of intelligent video surveillance systems that monitor the ship’s perimeter in real time and trigger the relative alarms that initiate the rescue mission. In terms of deep learning analysis, since man overboard incidents occur rarely, they present a severe class imbalance problem, and thus, supervised classification methods are not suitable. To tackle this obstacle, we follow an alternative philosophy and present a novel deep learning framework that formulates man overboard identification as an anomaly detection task. The proposed system, in the absence of training data, utilizes a multi-property spatiotemporal convolutional autoencoder that is trained only on the normal situation. We explore the use of RGB video sequences to extract specific properties of the scene, such as gradient and saliency, and utilize the autoencoders to detect anomalies. To the best of our knowledge, this is the first time that man overboard detection is made in a fully unsupervised manner while jointly learning the spatiotemporal features from RGB video streams. The algorithm achieved 97.30% accuracy and a 96.01% F1-score, surpassing the other state-of-the-art approaches significantly.
Deep autoencoder based domain adaptation for transfer learning

The concept of transfer learning has received a great deal of concern and interest throughout the last decade. Selecting an ideal representational framework for instances of various domains to minimize the divergence among source and target domains is a fundamental research challenge in representative transfer learning. The domain adaptation approach is designed to learn more robust or higher-level features, required in transfer learning. This paper presents a novel transfer learning framework that employs a marginal probability-based domain adaptation methodology followed by a deep autoencoder. The proposed frame adapts the source and target domain by plummeting distribution deviation between the features of both domains. Further, we adopt the deep neural network process to transfer learning and suggest a supervised learning algorithm based on encoding and decoding layer architecture. Moreover, we have proposed two different variants of the transfer learning techniques for classification, which are termed as (i) Domain adapted transfer learning with deep autoencoder-1 (D-TLDA-1) using the linear regression and (ii) Domain adapted transfer learning with deep autoencoder-2 (D-TLDA-2) using softmax regression. Simulations have been conducted with two popular real-world datasets: ImageNet datasets for image classification problem and 20_Newsgroups datasets for text classification problem. Experimental findings have established and the resulting improvements in accuracy measure of classification shows the supremacy of the proposed D-TLDA framework over prominent state-of-the-art machine learning and transfer learning approaches.
Real-Time Ship Tracking under Challenges of Scale Variation and Different Visibility Weather Conditions

Visual ship tracking provides crucial kinematic traffic information to maritime traffic participants, which helps to accurately predict ship traveling behaviors in the near future. Traditional ship tracking models obtain a satisfactory performance by exploiting distinct features from maritime images, which may fail when the ship scale varies in image sequences. Moreover, previous frameworks have not paid much attention to weather condition interferences (e.g., visibility). To address this challenge, we propose a scale-adaptive ship tracking framework with the help of a kernelized correlation filter (KCF) and a log-polar transformation operation. First, the proposed ship tracker employs a conventional KCF model to obtain the raw ship position in the current maritime image. Second, both the previous step output and ship training sample are transformed into a log-polar coordinate system, which are further processed with the correlation filter to determine ship scale factor and to suppress the negative influence of the weather conditions. We verify the proposed ship tracker performance on three typical maritime scenarios under typical navigational weather conditions (i.e., sunny, fog). The findings of the study can help traffic participants efficiently obtain maritime situation awareness information from maritime videos, in real time, under different visibility weather conditions.
Hypothesis Formalization: Empirical Findings, Software Limitations, and Design Implications

Data analysis requires translating higher level questions and hypotheses into computable statistical models. We present a mixed-methods study aimed at identifying the steps, considerations, and challenges involved in operationalizing hypotheses into statistical models, a process we refer to as hypothesis formalization . In a formative content analysis of 50 research papers, we find that researchers highlight decomposing a hypothesis into sub-hypotheses, selecting proxy variables, and formulating statistical models based on data collection design as key steps. In a lab study, we find that analysts fixated on implementation and shaped their analyses to fit familiar approaches, even if sub-optimal. In an analysis of software tools, we find that tools provide inconsistent, low-level abstractions that may limit the statistical models analysts use to formalize hypotheses. Based on these observations, we characterize hypothesis formalization as a dual-search process balancing conceptual and statistical considerations constrained by data and computation and discuss implications for future tools.
A novel nomad migration-inspired algorithm for global optimization

Nature-inspired computing (NIC) has been widely studied for many optimization scenarios. However, miscellaneous solution space of real-world problem causes it is challenging to guarantee the global optimum. Besides, cumbersome structure and complex parameters setting-up make the existed algorithms hard for most users who are not specializing in NIC, to understand and use. To alleviate these limitations, this paper devises a succinct and efficient optimization algorithm called Nomad Algorithm (NA). It is inspired by the migratory behavior of nomadic tribes on the prairie. Extensive experiments are implemented with respects to accuracy, rate, stability, and cost of optimization. Mathematical proof is given to guarantee the global convergence, and the nonparametric tests are conducted to confirm the significance of experiment results. The statistical results of optimization accuracy denote NA outperforms its rivals for most cases (23/28) by orders of magnitude significantly. It is considered as a promising optimizer with excellent efficiency and adaptability.
3D surface reconstruction of transparent objects using laser scanning with a four-layers refinement process

Acquiring the 3D geometry of objects has been an active research topic, wherein the reconstruction of transparent objects poses a great challenge. In this paper, we present a fully automatic approach for reconstructing the exterior surface of a complex transparent scene. Through scanning a line laser by a galvo-mirror, images of the scene are captured from two viewing directions. Due to the light transmission inside the transparent object, the captured feature points and the calibrated laser plane can produce large number of 3D point candidates with large incorrect points through direct triangulation. Various situations of laser transmission inside the transparent object are analyzed and the reconstructed 3D laser point candidates are classified into two types: first-reflection points and non-first-reflection points. The first-reflection points means the first reflected laser points on the front surface of measured objects. Then, a novel four-layers refinement process is proposed to extract the first-reflection points step by step from the 3D point candidates through optical geometric constraints, including (1) Layer-1 : fake points removed by single camera, (2) Layer-2 : ambiguity points removed by the dual-camera joint constraint, (3) Layer-3 : retrieve the missing first-reflection exterior surface points by fusion and (4) Layer-4 : severe ambiguity points removed by contour-continuity. Besides, a novel calibration model about this imaging system is proposed for 3D point candidates reconstruction through triangulation. Compared with traditional laser scanning method, we pulled in the viewing angle information of the second camera and a novel four-layers refinement process is adopted for reconstruction of transparent objects. Various experiments on real objects demonstrate that proposed method can successfully extract the first-reflection points from the candidates and recover the complex shapes of transparent and semitransparent objects.
Model Specification Searches in Structural Equation Modeling with a Hybrid Ant Colony Optimization Algorithm

Model specification is a crucial aspect of structural equation modeling (SEM), since a misspecified model may lead to biased parameter estimation and result in inaccurate conclusions. We propose the Hybrid Ant Colony Optimization Algorithm (hACO), an improved metaheuristic algorithm to conduct model specification searches in SEM. This data mining algorithm combines aspects of the Ant Colony Optimization algorithm with the Tabu search algorithm to increase both accuracy and efficiency. A Monte Carlo simulation study showed that the hACO algorithm provided accurate and efficient SEM specification searches across all designed simulation conditions. The hACO algorithm can help applied researchers conduct specification searches while avoiding potential model misspecifications.
A Soar-Based Space Exploration Algorithm for Mobile Robots

Space exploration is a hot topic in the application field of mobile robots. Proposed solutions have included the frontier exploration algorithm, heuristic algorithms, and deep reinforcement learning. However, these methods cannot solve space exploration in time in a dynamic environment. This paper models the space exploration problem of mobile robots based on the decision-making process of the cognitive architecture of Soar, and three space exploration heuristic algorithms (HAs) are further proposed based on the model to improve the exploration speed of the robot. Experiments are carried out based on the Easter environment, and the results show that HAs have improved the exploration speed of the Easter robot at least 2.04 times of the original algorithm in Easter, verifying the effectiveness of the proposed robot space exploration strategy and the corresponding HAs.
Situated Visual Analysis and Live Monitoring for Manufacturing

Modern machines continuously log status reports over long periods of time, which are valuable data to optimize working routines. Data visualization is a commonly used tool to gain insights into these data, mostly in retrospective (e.g., to determine causal dependencies between the faults of different machines). We present an approach to bring such visual analyses to the shop floor to support reacting to faults in real time. This approach combines spatio-temporal analyses of time series using a handheld touch device with augmented reality for live monitoring. Important information augments machines directly in their real-world context, and detailed logs of current and historical events are displayed on the handheld device. In collaboration with an industry partner, we designed and tested our approach on a live production line to obtain feedback from operators. We compare our approach for monitoring and analysis with existing solutions that are currently deployed.
Grassmann manifold based framework for automated fall detection from a camera

In this paper, we propose a Grassmann manifold based novel, real-time framework for automated fall detection in an indoor environment using a single camera. Fall is an activity that is uncontrolled, unintentional, involuntary and can occur while a person is doing any daily living activity. This is especially true for the elderly and the sick, for whom a fall can lead to further complications that may cause irreversible damage to their health. Therefore, it is important to develop a non-intrusive, automated fall detection method such that an alert can be raised in case a fall occurs. We propose a Grassmann manifold based framework for fall detection from a single camera that is also capable of recognizing other daily living activities (DLA) such as walking, sitting, etc. We perform experiments using publicly available datasets and our experimental results show that the fall detection and recognition accuracy of our proposed framework is comparable with the state of the art.
Few-Shot Domain Adaptation via Mixup Optimal Transport

Unsupervised domain adaptation aims to learn a classification model for the target domain without any labeled samples by transferring the knowledge from the source domain with sufficient labeled samples. The source and the target domains usually share the same label space but are with different data distributions. In this paper, we consider a more difficult but insufficient-explored problem named as few-shot domain adaptation, where a classifier should generalize well to the target domain given only a small number of examples in the source domain. In such a problem, we recast the link between the source and target samples by a mixup optimal transport model. The mixup mechanism is integrated into optimal transport to perform the few-shot adaptation by learning the cross-domain alignment matrix and domain-invariant classifier simultaneously to augment the source distribution and align the two probability distributions. Moreover, spectral shrinkage regularization is deployed to improve the transferability and discriminability of the mixup optimal transport model by utilizing all singular eigenvectors. Experiments conducted on several domain adaptation tasks demonstrate the effectiveness of our proposed model dealing with the few-shot domain adaptation problem compared with state-of-the-art methods.
Constructing Digital Literacy Instrument and its Effect on College Students' Learning Outcomes

This research aims to develop and test the effectiveness of digital literacy instruments on students' learning outcomes.This study is a mixed method that combines survey and pre-experimental research.The survey research was conducted through a quantitative approach using a questionnaire as the research instrument with assessed numerical items.The survey respondents consisted of 318 students from various universities in Indonesia who were taken through cluster random sampling technique.The implementation of the instrument was carried out through a pre-experimental design to 103 students to test the instrument's effectiveness.The confirmatory factor analysis result shows that the model forms three factors, which are factor 1 (communicating digital content), factor 2 (exploring digital content), and factor 3 (creating and using digital content).The result of the goodness of fit model shows that the instrument has met all the criteria with a value of X 2 /df = 1.642 (<3.00),RMSEA = .079(≤ .08),GFI = .900(≥ .90),AGFI = .825(≥ .90),TLI = .939(≥ .90),and CFI = .951(≥ .90).Empirically the instrument has good discrimination power so that it can measure students' digital literacy skills at different academic ability levels (Sig.<.05).In addition, the instrument has been effectively used to measure and predict student learning outcomes (R square = .255,Sig.< .05).The instruments' result is relevant to current conditions and can further explore students' digital literacy skills.
A novel multi-task twin-hypersphere support vector machine for classification

Both regularized multi-task learning (RMTL) and direct multi-task twin support vector machine (DMTSVM) have shown good performances in dealing with multi-task problems. They all use hyperplanes to realize classification. However, the hyperplane cannot reflect the distribution of data well. Therefore, in this paper, we propose a novel multi-task twin hypersphere support vector machine (MTTHSVM) to solve multi-task classification problems. It will generate two hyperspheres rather than hyperplanes for each task. So, the proposed method could better describe the distribution information of all training samples compared with the existing RMTL and DMTSVM. Based on Hierarchical Bayes theory, MTTHSVM divides the center of each hypersphere into task-specific and task-common parts to better measure the commonality and individuality of tasks. Then the shared information contained in multiple related tasks could be adaptively mined well. Therefore, the prediction accuracy will be improved to some extent. Besides, MTTHSVM is superior to RMTL and DMTSVM in terms of computational efficiency. This is because our MTTHSVM just solves two smaller-sized quadratic programming problems without any matrix inverse operations. Experimental results on one artificial data set, thirty-five benchmark data sets and a real image data set Cifar100 have verified the effectiveness of our method.
Reinforcement Learning Your Way: Agent Characterization through Policy Regularization

The increased complexity of state-of-the-art reinforcement learning (RL) algorithms have resulted in an opacity that inhibits explainability and understanding. This has led to the development of several post-hoc explainability methods that aim to extract information from learned policies thus aiding explainability. These methods rely on empirical observations of the policy and thus aim to generalize a characterization of agents' behaviour. In this study, we have instead developed a method to imbue a characteristic behaviour into agents' policies through regularization of their objective functions. Our method guides the agents' behaviour during learning which results in an intrinsic characterization; it connects the learning process with model explanation. We provide a formal argument and empirical evidence for the viability of our method. In future work, we intend to employ it to develop agents that optimize individual financial customers' investment portfolios based on their spending personalities.
Situated, Yet Silent: Data Relations in Smart Street Furniture

This article provides new evidence of the ways that smart cities materialize within specific sites and contexts through smart street furniture (SSF). Drawing on empirical data generated through mixed-method field research, the article examines the situated data relations that emerge in the context of the adoption of InLinkUK smart kiosks in Glasgow and Strawberry Energy smart benches in London. The concept of "silences" is proposed to analyze insufficiently articulated data relations resulting from gaps or absences in the use, design, and governance of this new type of urban furniture. The argument made is that data silences lead to failures to account for decisions and the deferral of responsibilities regarding the data aspects of these objects. It is suggested that an approach that focuses on "listening" to and "speaking" about data relations can enable dialogical forms of accountability, and realize the potential of SSF for citizens in local contexts.
A Novel Approach of Object Detection using Deep Learning for Animal Safety

This paper uses convolutional neural networks (CNN) algorithm to detect animals. Four different Object Detection frameworks are implemented and compared their performance with our novel approach. Many algorithms of convolutional neural networks like RCNN, Faster RCNN, Single Shot detection, YOLOv3 are used in object detection tasks are compared. A customized Faster RCNN is designed with anchor manipulations This framework is found to be best suited technique for Animal Detection System giving the best time-accuracy trade-off. Experimental results shows that this method has reduced the best inference time significantly.
A comparative performance study of random‐grid model for hyperparameters selection in detection of abnormalities in digital breast images

Abstract Deep learning models have been widely reported to have achieved significant performance in image processing and classification tasks. They have mainly been harnessed and applied to the problem of detecting abnormalities in digital breast images. However, the significant number and high dimensional space requirement of hyperparameters in deep learning models often make it challenging to find the best configuration for such parameters when tuning for best performance. In appropriately handling this performance tuning often lead to difficulty in striking balance between underfitting and overfitting. This article proposes an optimized convolutional neural network (CNN) architecture through application of hybrid selection model in obtaining best hyperparameter configuration which outperforms similar existing models. We approached this non‐trivial challenge by defining a hybrid of the grid‐based and random‐based model for the selection of hyperparameters and then investigate the performance of the configurations. To further improve the performance of the CNN model, data augmentation technique was applied. Furthermore, the study undertook a comparative study of the performance of the best configuration on some benchmarked datasets. The resulting model was applied to publicly available benchmark datasets, namely, the DDSM and MIAS datasets. Findings from the experimentations revealed that hyperparameters with Adam optimization algorithm showed superiority by yielding an accuracy of 1.0 using DDSM dataset, while SGD, RMSprop, Adam, and Adagrad output an accuracy of 0.9375 with MIAS dataset. The outcome of this study further strengthens the appropriateness of Adam optimizer and has also produced a state‐of‐the‐art CNN model suitable for solving the problem of detection and classification of breast cancer from digital mammography.
FPGA implementation of hybrid recursive reversable box filter-based fast adaptive bilateral filter for image denoising

A Bilateral filter (BF) is a kind of edge-preserving filter, which is mostly used in computer vision, computational photography, and image processing applications. Recently, Reversible Logic Gates (RLG) technology has emerged as an attractive area in the field of field-programmable gate array (FPGA) based implementations. The RLG technology consumes less Quantum Cost (QC) as compared to conventional CMOS-based logic gates. As a result, this paper proposed an FPGA implementation of a hybrid recursive reversible box filter (HRRBF)-based fast adaptive bilateral filter (FABF) using RLG technology with joint geometric and photometric weight calculation and kernel result calculation operations. The RLGs are used to develop the reversable modified carry select adder (RMCSA), reversable adder subtractor (RAS), and multiplier, which significantly improves the speed and reduces the execution time, area, power, and delay as compared to the basic methods. In addition, ternary content addressable memory (TCAM) is used as the main memory to improve the speed of filtering. Obtained hardware and simulation results show that proposed RLG-based HRRBF-FABF results in enhanced performance as compared to state-of-the-art architectures. In addition, the image denoising application was also implemented with the proposed RLG-based HRRBF-FABF and achieved structural similarity (SSIM) index of 0.999 and peak signal-to-noise ratio (PSNR) of 78.348 dB. The simulation results showed that the proposed method resulted in better denoising performance as compared to conventional filtering approaches.