Reviewerly/exHarmony huggingface dataset:
More than meets the eye: use of computer vision algorithms to identify stone tool material through the analysis of cut mark micro-morphology

Abstract Artificial intelligence algorithms have recently been applied to taphonomic questions with great success, outperforming previous methods of bone surface modification (BSM) identification. Following these new developments, here we try different deep learning model architectures, optimizers and activation functions to assess if it is possible to identify a stone tool’s raw material simply by looking at the cut marks that it created on bone. The deep learning models correctly discerned between flint, sandstone and quartzite with accuracy rates as high as 78%. Also, single models seem to work better than ensemble ones, and there is no optimal combination of hyperparameters that perform better in every possible scenario. Model fine-tuning is thus advised as a protocol. These results consolidate the potential of deep learning methods to make classifications out of BSM’s microscopic features with a higher degree of confidence and more objectively than alternative taphonomic procedures.
Numerical Study of Parallel Optoelectronic Reservoir Computing to Enhance Nonlinear Channel Equalization

Nonlinear impairment is one of the critical limits to enhancing the performance of high-speed communication systems. Traditional digital signal processing (DSP)-based nonlinear channel equalization schemes are influenced by limited bandwidth, high power consumption, and high processing latency. Optoelectronic reservoir computing (RC) is considered a promising optical signal processing (OSP) technique with merits such as large bandwidth, high power efficiency, and low training complexity. In this paper, optoelectronic RC was employed to solve the nonlinear channel equalization problem. A parallel optoelectronic RC scheme with a dual-polarization Mach–Zehnder modulator (DPol-MZM) is proposed and demonstrated numerically. The nonlinear channel equalization performance was greatly enhanced compared with the traditional optoelectronic RC and the Volterra-based nonlinear DSP schemes. In addition, the system efficiency was improved with a single DPol-MZM.
Depth Estimation for Colonoscopy Images with Self-supervised Learning from Videos

Depth estimation in colonoscopy images provides geometric clues for downstream medical analysis tasks, such as polyp detection, 3D reconstruction, and diagnosis. Recently, deep learning technology has made significant progress in monocular depth estimation for natural scenes. However, without sufficient ground truth of dense depth maps for colonoscopy images, it is significantly challenging to train deep neural networks for colonoscopy depth estimation. In this paper, we propose a novel approach that makes full use of both synthetic data and real colonoscopy videos. We use synthetic data with ground truth depth maps to train a depth estimation network with a generative adversarial network model. Despite the lack of ground truth depth, real colonoscopy videos are used to train the network in a self-supervision manner by exploiting temporal consistency between neighboring frames. Furthermore, we design a masked gradient warping loss in order to ensure temporal consistency with more reliable correspondences. We conducted both quantitative and qualitative analysis on an existing synthetic dataset and a set of real colonoscopy videos, demonstrating the superiority of our method on more accurate and consistent depth estimation for colonoscopy images.
Minimum-time velocity profile planning for planar motion considering velocity, acceleration and jerk constraints

This paper deals with a computationally efficient velocity profile planning of wheeled mobile robots restricted to a planar motion. The resulting profile complies with velocity, acceleration, and jerk constraints, all expressed in tangential and radial forms. The first part of the proposed algorithm introduces turning points and complies with velocity and acceleration constraints. Around these points, local velocity profiles are calculated from which the global minimum-time velocity profile is determined. In the second step of the algorithm, the existing velocity profile is modified to also comply with jerk constraints. This procedure distinguishes between single-point and interval jerk violations. The main novelties of the proposed algorithm are a computationally efficient two-step approach and consistent full jerk treatment. We have also provided a comparison with an established state-of-the-art method and statistical analysis to demonstrate the low computational burden. The proposed algorithm has many applications in time-critical transportations.
A Visual Attention Model Based on Eye Tracking in 3D Scene Maps

Visual attention plays a crucial role in the map-reading process and is closely related to the map cognitive process. Eye-tracking data contains a wealth of visual information that can be used to identify cognitive behavior during map reading. Nevertheless, few researchers have applied these data to quantifying visual attention. This study proposes a method for quantitatively calculating visual attention based on eye-tracking data for 3D scene maps. First, eye-tracking technology was used to obtain the differences in the participants’ gaze behavior when browsing a street view map in the desktop environment, and to establish a quantitative relationship between eye movement indexes and visual saliency. Then, experiments were carried out to determine the quantitative relationship between visual saliency and visual factors, using vector 3D scene maps as stimulus material. Finally, a visual attention model was obtained by fitting the data. It was shown that a combination of three visual factors can represent the visual attention value of a 3D scene map: color, shape, and size, with a goodness of fit (R2) greater than 0.699. The current research helps to determine and quantify the visual attention allocation during map reading, laying the foundation for automated machine mapping.
Multicomponent Measurement of Respirable Quartz, Kaolinite and Coal Dust using Fourier Transform Infrared Spectroscopy (FTIR): A Comparison Between Partial Least Squares and Principal Component Regressions

Abstract Exposure to respirable crystalline silica (RCS) is potentially hazardous to the health of thousands of workers in Great Britain. Both X-ray diffraction (XRD) and Fourier transform infrared (FTIR) spectroscopy can be used to measure RCS to assess exposures. The current method outlined in the Health and Safety Executive’s (HSE) Methods for the Determination of Hazardous Substances (MDHS) guidance series is ‘MDHS 101 Crystalline silica in respirable airborne dust - Direct-on-filter analyses by infrared spectroscopy or x-ray’. This describes a procedure for the determination of time-weighted average concentrations of RCS either as quartz or cristobalite in airborne dust. FTIR is more commonly employed because it is less expensive, potentially portable and relatively easy to use. However, the FTIR analysis of RCS is affected by spectral interference from silicates. Chemometric techniques, known as Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR), are two computational processes that have the capability to remove spectral interference from FTIR spectra and correlate spectral features with constituent concentrations. These two common chemometric processes were tested on artificial mixtures of quartz and kaolinite in coal dust using the same commercially available software package. Calibration, validation and prediction samples were prepared by collecting aerosols of these dusts onto polyvinylchloride (PVC) filters using a Safety in Mines Personal Dust Sampler (SIMPEDS) respirable cyclone. PCR and PLSR analyses were compared when processing the same spectra. Good correlations between the target values, measured using XRD, were obtained for both the PCR and PLSR models e.g. 0.98–0.99 (quartz), 0.98–0.98 (kaolinite) and 0.96–0.97 (coal). The level of agreement between PCR and PLSR was within the 95% confidence value for each analyte. Slight differences observed between predicted PCR and PLSR values were due to the number of optimal principal components applied to each chemometric process. The presence of kaolinite in these samples caused an 18% overestimation of quartz, for the FTIR, when following MDHS 101 without a chemometric method. Chemometric methods are a useful approach to obtain interference-free results for the measurement of RCS from some workplace environments and to provide a multicomponent analysis to better characterise exposures of workers.
Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus

In this paper, we propose a multi-view stereo matching method, EnSoft3D (Enhanced Soft 3D Reconstruction) to obtain dense and high-quality depth images. Multi-view stereo is one of the high-interest research areas and has wide applications. Motivated by the Soft3D reconstruction method, we introduce a new multi-view stereo matching scheme. The original Soft3D method is introduced for novel view synthesis, while occlusion-aware depth is also reconstructed by integrating the matching costs of the Plane Sweep Stereo (PSS) and soft visibility volumes. However, the Soft3D method has an inherent limitation because the erroneous PSS matching costs are not updated. To overcome this limitation, the proposed scheme introduces an update process of the PSS matching costs. From the object surface consensus volume, an inverse consensus kernel is derived, and the PSS matching costs are iteratively updated using the kernel. The proposed EnSoft3D method reconstructs a highly accurate 3D depth image because both the multi-view matching cost and soft visibility are updated simultaneously. The performance of the proposed method is evaluated by using structured and unstructured benchmark datasets. Disparity error is measured to verify 3D reconstruction accuracy, and both PSNR and SSIM are measured to verify the simultaneous enhancement of view synthesis.
Blockchain Technology Adoption by Malaysian Higher Education Institutes: A Perspective of Integrated Tam Model and Toe Framework

The purpose of this research is to identify and investigate the factors that influence Blockchain technology adoption in Malaysian higher education sector.This research has proposed a theoretical framework for Blockchain technology adoption based on Integrated TAM model and TOE framework constructs.The seven factors examined in this research are relative advantage, perceived ease of use, perceived usefulness, scalability concern, top management support, competitive pressure and regulatory policy.Factors are categorized in the Technological, organizational and environmental context.A questionnaire-based survey was used to collect the primary data for this research, from five Malaysian higher education institutes.Hypothesis for this research were derived using the proposed research framework and previously published literature, and further tested using multiple linear regression.The findings of the statistical analysis reveal that, perceived usefulness, competitive pressure and top management support have a significant and positive influence on Malaysian higher education institutes intention to adopt Blockchain technology.This research was performed in the Malaysian higher education sector, so this may limit the generalizability of the relevant findings.The findings of this research provide a better understanding of which technological, organizational and environmental factors affect the Blockchain adoption process in the Malaysian higher education institutes.This research contributes to the theoretical body of knowledge of the technology adoption, by examining unique external variables with an integrated TAM model and TOE framework approach.
Contrastive blind denoising autoencoder for real time denoising of industrial IoT sensor data

In an industrial IoT setting, ensuring the quality of sensor data is a must when data-driven algorithms operate on the upper layers of the control system. Unfortunately, the common place in industrial facilities is to find sensor time series heavily corrupted by noise and outliers. This work proposes a purely data-driven self-supervised learning-based approach, based on a blind denoising autoencoder, for real time denoising of industrial sensor data. The term blind stresses that no prior knowledge about the noise is required for denoising, in contrast to typical denoising autoencoders. Blind denoising is achieved by using a noise contrastive estimation (NCE) regularization on the latent space of the autoencoder, which not only helps to denoise but also induces a meaningful and smooth latent space that can be exploited in other downstream tasks. Experimental evaluation in both a simulated system and a real industrial process shows that the proposed technique outperforms classical denoising methods.
Optimal path planning of autonomous navigation in outdoor environment via heuristic technique

Path planning for automated guided vehicles (AGVs) operating in an outdoor environment is a key task in logistic applications. This paper highlights a new approach for finding collision-free path for AGVs in an outdoor environment using satellite images. Satellite images are transformed into occupancy grids that divide the environment, obstacles (building, trees and pedestrian path etc.,) as black grids and free routes as white grids in order to find an obstacle free path. The proposed navigation control strategy is to localise the AGVs vehicle route until any approaching obstacle is detected and to turn to the safest area before continuing its path. The navigational model was developed in the MATLAB/Simulink 2D virtual environment to visualise the path navigation. Particle swarm optimization and its five variants were used to obtain the obstacle free path. Experiments were conducted in various environments to verify the efficiency and performance of the algorithm. Validation with a bench mark instance ensures that the proposed algorithm found a shorter path with less computation time.
Weakly Supervised Human-Object Interaction Detection in Video via Contrastive Spatiotemporal Regions

We introduce the task of weakly supervised learning for detecting human and object interactions in videos. Our task poses unique challenges as a system does not know what types of human-object interactions are present in a video or the actual spatiotemporal location of the human and the object. To address these challenges, we introduce a contrastive weakly supervised training loss that aims to jointly associate spatiotemporal regions in a video with an action and object vocabulary and encourage temporal continuity of the visual appearance of moving objects as a form of self-supervision. To train our model, we introduce a dataset comprising over 6.5k videos with human-object interaction annotations that have been semi-automatically curated from sentence captions associated with the videos. We demonstrate improved performance over weakly supervised baselines adapted to our task on our video dataset.
The Perron Gold Deposit, Archean Abitibi Belt, Canada: Exceptionally High-Grade Mineralization Related to Higher Gold-Carrying Capacity of Hydrocarbon-Rich Fluids

The Perron deposit, an Archean orogenic gold deposit located in the Abitibi belt, hosts a quartz vein-type gold-bearing zone, known as the high-grade zone (HGZ). The HGZ is vertically continuous along &gt;1.2 km, and is exceptionally rich in visible gold throughout its vertical extent, with grades ranging from 30 to 500 ppm. Various hypotheses were tested to account for that, such as: (1) efficient precipitating mechanisms; (2) gold remobilization; (3) particular fluids; (4) specific gold sources for saturating the fluids; and (5) a different mineralizing temperature. Host rocks recorded peak metamorphism at ~600 °C based on an amphibole geothermometer. Visible gold is associated with sphalerite (&lt;5%) which precipitated at 370 °C, based on the sphalerite GGIMFis geothermometer, during late exhumation of verticalized host rocks. Pyrite chemistry analyzed by LA-ICP-MS (Laser Ablation Inductively Coupled Plasma Mass Spectrometry) is comparable to classical orogenic gold deposits of the Abitibi belt, without indication of a possible magmatic fluid and gold contribution. Comparison of pyrite trace element signatures for identifying a potential gold source was inconclusive to demonstrate that primary base-metal rich volcanogenic gold mineralization, dispersed in the host rhyolitic dome, could be the source for the later formation of the HGZ. Rather, nodular pyrites in graphitic shales, sharing similar trace element signatures with pyrite of the HGZ, are considered a potential source. The most striking outcome is the lack of water in the mineralizing fluids, implying that gold was not transported under aqueous complexes, even if fugacity of sulfur (−6) and oxygen (−28), and pH (~7) are providing the best conditions at a temperature of 350 °C for solubilizing gold in water. Fluid inclusions, analyzed by solid-probe mass spectrometry, are rather comparable to fossil gas composed mostly of hydrocarbons (methane and ethane and possibly butane and propane and other unidentified organic compounds), rich in CO2, with N2 and trace of Ar, H2S, and He. It is interpreted that gold and zinc were transported as hydrocarbon-metal complexes or as colloidal gold nanoparticles. The exceptional high content of gold and zinc in the HGZ is thus explained by the higher transporting capacity of these unique mineralizing fluids.
STUDI FENOMENOLOGI PENGGUNAAN E-MODUL DALAM PEMBELAJARAN DARING MUATAN IPA DI SD MUHAMMADIYAH 5 JAKARTA

The aim of this study is to describe the implementation of E-modules utilization in online learning of natural science content at SD Muhammadiyah 5 Jakarta. This research was conducted at SD Muhammadiyah 5 Jakarta. The subjects of this study are fifth grade students of SD Muhammadiyah 5 Jakarta. The approach used in this research is qualitative approach with phenomenological methods. Data collection was through observation, interview, and documentation with the data analysis technique used was the Miles and Huberman model. The results showed that the implementation of E-modules in online learning of natural science content in class V SD Muhammadiyah 5 Jakarta as an effective learning resource and support the students at learning, that helps during online learning, stimulating students and motivating students to learn, and achieving learning goals. The e-module is a web-based module which contains material with pictures, videos and edugames that make learning more engaging for students. The application of E-module received positive responses from various parties, that is students, parents, and outsiders such as other schools.
Pitfalls and Solutions for Technical Debt Management in Agile Software Projects

This article presents technical debt (TD) impediments, decision factors, enabling practices, and actions diagrams for TD management in agile software projects. By analyzing diagrams, professionals can avoid the pitfalls, and increase their capacity, for better TD management.
μSCOPE: A Methodology for Analyzing Least-Privilege Compartmentalization in Large Software Artifacts

By prioritizing simplicity and portability, least-privilege engineering has been an afterthought in OS design, resulting in monolithic kernels where any exploit leads to total compromise. μSCOPE (“microscope”) addresses this problem by automatically identifying opportunities for least-privilege separation. μSCOPE replaces expert-driven, semi-automated analysis with a general methodology for exploring a continuum of security vs. performance design points by adopting a quantitative and systematic approach to privilege analysis. We apply the μSCOPE methodology to the Linux kernel by (1) instrumenting the entire kernel to gain comprehensive, fine-grained memory access and call activity; (2) mapping these accesses to semantic information; and (3) conducting separability analysis on the kernel using both quantitative privilege and overhead metrics. We discover opportunities for orders of magnitude privilege reduction while predicting relatively low overheads—at 15% mediation overhead, overprivilege in Linux can be reduced up to 99.8%—suggesting fine-grained privilege separation is feasible and laying the groundwork for accelerating real privilege separation.
Facial Prior Based First Order Motion Model for Micro-expression Generation

Spotting facial micro-expression from videos finds various potential applications in fields including clinical diagnosis and interrogation, meanwhile this task is still difficult due to the limited scale of training data. To solve this problem, this paper tries to formulate a new task called micro-expression generation and then presents a strong baseline which combines the first order motion model with facial prior knowledge. Given a target face, we intend to drive the face to generate micro-expression videos according to the motion patterns of source videos. Specifically, our new model involves three modules. First, we extract facial prior features from a region focusing module. Second, we estimate facial motion using key points and local affine transformations with a motion prediction module. Third, expression generation module is used to drive the target face to generate videos. We train our model on public CASME II, SAMM and SMIC datasets and then use the model to generate new micro-expression videos for evaluation. Our model achieves the first place in the Facial Micro-Expression Challenge 2021 (MEGC2021), where our superior performance is verified by three experts with Facial Action Coding System certification. Source code is provided in https://github.com/Necolizer/Facial-Prior-Based-FOMM.
An Improved Rapidly-Exploring Random Trees Algorithm Combining Parent Point Priority Determination Strategy and Real-Time Optimization Strategy for Path Planning

In order to solve the problems of long path planning time and large number of redundant points in the rapidly-exploring random trees algorithm, this paper proposed an improved algorithm based on the parent point priority determination strategy and the real-time optimization strategy to optimize the rapidly-exploring random trees algorithm. First, in order to shorten the path-planning time, the parent point is determined before generating a new point, which eliminates the complicated process of traversing the random tree to search the parent point when generating a new point. Second, a real-time optimization strategy is combined, whose core idea is to compare the distance of a new point, its parent point, and two ancestor points to the target point when a new point is generated, choosing the new point that is helpful for the growth of the random tree to reduce the number of redundant points. Simulation results of 3-dimensional path planning showed that the success rate of the proposed algorithm, which combines the strategy of parent point priority determination and the strategy of real-time optimization, was close to 100%. Compared with the rapidly-exploring random trees algorithm, the number of points was reduced by more than 93.25%, the path planning time was reduced by more than 91.49%, and the path length was reduced by more than 7.88%. The IRB1410 manipulator was used to build a test platform in a laboratory environment. The path obtained by the proposed algorithm enables the manipulator to safely avoid obstacles to reach the target point. The conclusion can be made that the proposed strategy has a better performance on optimizing the success rate, the number of points, the planning time, and the path length.