Reviewerly/exHarmony huggingface dataset:
Scaling Smart Contracts Via Layer-2 Technologies: Some Empirical Evidence

Blockchain-based smart contracts can potentially replace certain traditional contracts through decentralized enforcement and reduced transaction costs. However, scalability is a key bottleneck hindering their broader application and adoption, often leading to concentrated or exclusive networks. To avoid falling short of the original promise of the technology, firms actively explore "layer-2" methods for scaling. We provide some initial evidence on the economic implications of a layer-2 scaling solution, which moves information aggregation from on-chain to off-chain peer-to-peer networks. A parallel-system experiment allows clean identification because we observe the same unit in the treatment and control systems at the same time. We find that this scaling solution reduces operating costs by 76%, and importantly, leads to decentralization with lower market concentration and more participation, which in turn improves data accuracy. The findings provide initial evidence of how blockchain and smart contracting technologies evolve towards achieving decentralized and scalable trust.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
Bitcoin Mining Meets Wall Street: A Study of Publicly Traded Crypto Mining Companies

This paper studies the operations and financial valuations of 13 cryptocurrency mining companies that are listed on the NASDAQ stock exchange and have facilities in North America. We find that miners using Texas wind power are offline more than other miners, in a more erratic pattern, while receiving significant revenue augmentations from “curtailment” payments by electric utilities. Despite having relatively low activity levels, these Texas miners are more profitable than those using more stable sources of energy such as hyrdo power or solar power, as reflected in significantly higher enterprise values. We find a negative and significant beta between crypto mining stocks and an index of electric utilities, suggesting that ownership of a crypto mining company might provide a useful channel for risk management in the electric power industry.Institutional subscribers to the NBER working paper series, and residents of developing countries may download this paper without additional charge at www.nber.org.
CUDA implementation of the antlion optimization algorithm

A parallel version of Ant Lion Optimizer algorithm using the CUDA platform is introduced in this paper. Efficient kernel, memory and thread management approaches have been developed to maximize its performance. The new algorithm was tested against the canonical algorithm on 15 scalable problems of different sizes, with a total of 172 experiments. The solution costs and the execution times were compared using relative percentage difference and significance tests. The results showed the CUDA antlion algorithm significantly improves upon the execution time, while retaining the same solution quality.
Theoretical basis and occurrence of internet fraud victimisation: Based on two systems in decision-making and reasoning

The influencing factors of internet fraud, including demographics, psychology, experience and knowledge of susceptibility, have been widely studied. Research on the psychological mechanism of the victimisation process of internet fraud is relatively scarce but suggests a new research perspective. To summarise and unify the research in this field, this study systematically searched and analysed articles on the psychological decision-making mechanism of online fraud victims. We found that (a) previous researchers consistently believed that the heuristic processing mode was correlated with susceptibility to online fraud and that the systematic processing mode was helpful to detect and identify fraud. From the overall review results, we do not reject this conclusion, but the verification and intrinsic explanation of this relationship need to be further strengthened. (b) Under the heuristic-systematic model (HSM), with the exception of the trait of suspicion, there is no consensus on whether psychological factors (e.g., personality) influence the likelihood of online fraud through the mediating effect of the selection of the two systems. Objective knowledge and experience in specific fields have been found to be able to achieve this path. Information on the influential variables of equipment and habits is emerging, but how they affect network victimisation through the heuristic processing system needs to be further clarified. (c) The measurement of variables is conducted through simulation experiments. There may be a gap between the likelihood of internet fraud victimisation in the simulation experiment and in the real world. (d) The defence strategies under the HSM are intentional explorations, such as content-based cue recognition technology and simulated scene training.
Pre-Activating Semantic Information for Image Aesthetic Assessment

Automatic image aesthetic evaluation is an attractive and challenging visual task. Recently, methods based on convolutional neural networks have achieved remarkable performance. However, semantic information, an intuitive prerequisite for evaluating image aesthetics, has not received enough attention regarding its importance in previous methods. How to efficiently extract semantic information and make better use of it to assist the aesthetic evaluation task remains unsolved. In this article, we propose to utilize the self-supervised model Auto-Encoder to extract semantic information in the form of multi-task learning. Then, a fusing module is prepended at the bottleneck layer to explicitly combine semantic information with aesthetic information in a pre-activated manner. Specifically, we implement a customized pooling operation to pool the semantic features extracted by Auto-Encoder and apply a weak constraint between the pooled semantic features and aesthetic information to realize the combination. The following regressor can complete aesthetic evaluation based on the semantic–aesthetic combined features. In addition, to enable our model to adapt to arbitrary aspect ratios of images, another pooling strategy called spatial pyramid pooling is adopted to obtain the image features of a fixed length. Our method achieves competitive performance on the public image aesthetic evaluation benchmark. Especially on the most commonly used metric Spearman rank-order correlation coefficient, the proposed model achieved the best performance compared with some state-of-the-art methods. Extensive ablation studies and visualization experiments were conducted to demonstrate the effectiveness of our method.
SciTok and SciTube: Utilizing educational videos and short-flick experiments as lecture aids in the new normal learning set-up

Since concepts and principles being studied Science and technology are said to be complex for students specially in the times of crisis, the interests of researcher were geared toward investigating the effectiveness of engaging and acquiring concepts being taught in the said learning area as supplemented by educational videos and use of social media platform to spark the interest of learners. To find light, the researcher used the total enumeration of Grade 5 Set A ODL Learners at NAPICO Elementary School in Pasig City as respondents. Using the experimental descriptive research design, pretest and posttest were administered before and after the exposure to various educational YouTube videos and SciTok Experiments with topics aligned with the lessons in Science 5. One-shot-group testing was utilized and data that came from each student’s pretest and post-test scores were subjected to statistical treatment such as mean, standard deviation, and dependent T-Test to determine the differences in the mean scores. Findings of study showed that the overall mean results for pretest and posttest for which were 3.20 and 8.00 having Fairly Satisfactory and Very Satisfactory verbal interpretation. As seen, the mean of posttest was greater compared to pretest, this implied that there was an increase in the engagement and acquisition of Science 5 concepts after the exposure to YouTube educational videos and SciTok Experiments. Further, the dependent t-test results revealed that the performance in the said learning area after exposure to YouTube educational videos and SciTok Experiments differ significantly since the p-value is less than 0.05. With such results, the null hypothesis is rejected with verbal interpretation of significant. In conclusion, YouTube educational videos and SciTok Experiments help in improving the engagement and acquisition of concepts and scientific principles. However, continuous effort and emphasis on enhancing their motivation should be given to improve their scientific skills and explore more scientific experiments.
The area‐reconstruction h‐dome technique and its efficient Python implementation for improved particle size image analysis

The traditional watershed segmentation methods usually suffer from over segmentation for irregularly shaped particles. This is because the distance map of an irregularly shaped particle contains multiple local maxima, and over segmentation would happen if these local maxima were used as seeds for watershed segmentation. In this work, several methods based on morphological reconstruction, including h-dome transform, h-maxima, and area-reconstruction h-dome transform, are introduced to merge, or erase redundant local maxima, and the performance of these methods in avoiding over segmentation is compared. The results show that the area-reconstruction h-dome transform is the most effective method in controlling over segmentation among the evaluated methods. However, the area-reconstruction h-dome transform is achieved by superposition of binary reconstructions at each grayscale level, which is extremely time-consuming and impractical for batch processing. A hybrid pixel-queue algorithm is applied to accelerate the area-reconstruction h-dome transform, and the algorithm is implemented in Cython to further improve the computational efficiency. For a 2592 × 1944 pixel image, on a PC with an Intel Core i5 2.4GHz processor and 8 GB RAM, the processing time of the area-reconstruction h-dome transform after acceleration is about 549 ms, which is 249 times faster than the unaccelerated algorithm and 4 times faster than the reconstruction function in the Scikit-image library (an open-source image processing library for the Python programming language) which performs reconstruction by dilation. The accelerated area-reconstruction h-dome transform algorithm was successfully applied to the segmentation of rubber particles in a thermoplastic polyolefin (TPO) compound. RESEARCH HIGHLIGHTS: Techniques for segmenting particles with irregular shapes based on morphological reconstruction are reviewed. A fast algorithm for area-reconstruction h-dome transform is introduced based on Vincent's first approach combined with the pixel queue algorithm and Cython acceleration. The accelerated reconstruction algorithm is 249 times faster than the unaccelerated algorithm. The fast area-reconstruction h-dome transform algorithm is successfully applied to rubber particle segmentation of a thermal plastic polyolefin.
Improving the performance of graph based dependency parsing by guiding bi-affine layer with augmented global and local features

The growing interaction between humans and machines raises the necessity to more sophisticated tools for natural language understanding. Dependency parsing is crucial for capturing the semantics of a sentence. Although graph-based dependency parsing approaches outperform transition-based methods because they are not exposed to error propagation as their compeer, their feature space is comparatively limited. Thus, the main issue with graph-based parsing is how to expand the set of features to improve performance. In this research, we propose to expand the feature space of graph-based parsers. To benefit from the global meaning of the entire sentence content, we employee the sentence representation as an additional token feature. Also, to highlight local word collaborations that build sub-tree structures, we use convolutional neural network layers over token embeddings. We achieve the state-of-art results for Turkish, English, Hungarian, and Korean by getting the unlabeled and labeled attachment scores respectively on the test sets; 82.64% and 76.35% on Turkish IMST, 93.36% and 91.34% on English EWT, 90.85% and 87.39% on Hungarian Szeged, 92.44% and 89.58% on Korean GSD treebanks. Our experimental findings show that augmented global and local features empower the performance of graph-based dependency parsers.
Hand Anatomy and Neural Network-Based Recognition for Sign Language

This paper presents a model based on hand anatomy and neural network for the recognition of sign language. Feature extraction is done by using FAST and SIFT techniques. Out of these extracted features, only essential hand landmarks are selected using hand anatomy. NN is then used for the training and testing of the model. The proposed model is evaluated on sign language gestures used for medical purposes, general purposes, and family & relative purposes. The results prove that the proposed model has achieved fast and highly accurate results when compared with other available models. The model has achieved a recognizable accuracy of 99.85%, 97.55%, and 98.85%, on medical purposes, general purposes, and family & relative purposes, respectively.
Rapid assessment of out-of-plane nonlinear shear stress–strain response for thick-section composites using artificial neural networks and DIC

Thick-section fiber-reinforced polymer matrix composites (TSCs) have been increasingly used in primary and secondary load-bearing structures. Due to complex deformation mechanisms, accurate out-of-plane constitutive relations, including nonlinear shear stress–strain response, are required for structural analysis and failure prediction of TSC structures. A plate torsion test is proposed to assess the nonlinear out-of-plane shear stress–strain response using an artificial neural network (ANN) and digital image correlation (DIC). The ANN model was developed using three-dimensional finite element numerical simulations, and the out-of-plane shear stress was obtained with good accuracy. The efficiency of the shear stress calculation has been improved. The usability of the test method has been refined substantially. A comparison of the shear stress with the FEM-calculated result showed that the average error of the ANN-predicted shear stress was less than 0.5%. Accordingly, the constitutive parameters of a 15-ply thick S6C10/AC318 glass/epoxy unidirectional tape panel have been extracted from the minimization of the normalized error between ANN-calculated shear stress and DIC-measured shear strain iteratively. The efficiency of stress updating for the plate-torsion specimen has been improved by 105 times to extract the material constitutive parameters. Not limited to plate-torsion specimens, the proposed ANN-DIC method can be extended further for rapid assessment of material constitutive parameters for other anisotropic polymer-based composite materials using complex, statically indeterminate tests.
Demonstration of a simple and flexible calibration method for structured light system

The calibration of the system's geometric parameters is always an important step in 3D topography measurement based on fringe projection profilometry. However, the traditional calibration method needs to be realized by the implementation of an electric guide rail, which exhibits certain problems, such as complicated operation and inconvenient carrying. Along these lines, in this work, a new simple and flexible calibration method is introduced. For the proposed method, a round calibration board only needs to be moved arbitrarily many times within the depth of field of the camera and projector. By using the existing camera calibration method, each calibration plate position and pose can be derived, while the phase of every pixel and the height data, as well as the phase of every pixel-height mapping relationship, were established. In addition, the rational function fitting method was used to accurately estimate the phase distribution of the black area of the calibration plate, avoiding thus the influence of the phase artifacts caused by high-contrast calibration targets. The minimum root mean square error (RMSE) of the experimental results was 0.074 mm. Compared with the traditional method, our approach is more flexible and convenient, and has no loss of reliability.
Analyzing the Impact of Gender Misclassification on Face Recognition Accuracy

Automated face recognition technologies have been under scrutiny in recent years due to noted variations in accuracy relative to race and gender. Much of this concern was driven by media coverage of high error rates for women and persons of color reported in an evaluation of commercial gender classification ('gender from face”) tools. Many decried the conflation of errors observed in the task of gender classification with the task of face recognition. This motivated the question of whether images that are misclas-sified by a gender classification algorithm have increased error rate with face recognition algorithms. In the first experiment, we analyze the False Match Rate (FMR) of face recognition for comparisons in which one or both of the images are gender-misclassified. In the second experiment, we examine match scores of gender-misclassified images when compared to images from their labeled versus classified gender. We find that, in general, gender misclassified images are not associated with an increased FMR. For females, non-mated comparisons involving one misclassified image actually shift the resultant impostor distribution to lower similarity scores, representing improved accuracy. To our knowledge, this is the first work to analyze (1) the FMR of one- and two-misclassification error pairs and (2) non-mated match scores for misclassified images against labeled- and classified-gender categories.
SOCAR: Socially-Obtained CAR Dataset for Image Recognition in the Wild

While cars have become a significant object in computer vision applications, there are fewer spotlights on publicly-available car-related datasets. Among previously-proposed car datasets, we discover several improvement avenues. As most previous car datasets consist of web-crawled or surveillance camera-taken images, they are insufficient to illustrate various attributes, such as points of view or parts. Moreover, prior datasets primarily deal with a car model recognition task; thus, the scope of applicative studies was limited. To improve these avenues, we propose a Socially-Obtained CAR (SOCAR) dataset, a real-world car image dataset consisting of car images with more prosperous attributes. The key contributions of our study are as follows. First, under coordination with a large-scale car-sharing platform, we retrieve user-taken car images on both external and internal attributes and establish a dataset consisting of 10K images on 14 classes. Second, we design each class to represent a particular car's state; therefore, the SOCAR dataset enables the practitioners to solve various image recognition tasks such as understanding defects, dirt, or car wash. Lastly, we suggest baseline experiment results on the proposed dataset and experimentally examine the trained model effectively capture discriminative regions similar to human vision. We highly expect practitioners to use our SOCAR dataset for academic research on understanding car attributes or computer vision applications.
$k-\text{NN}$ embeded space conditioning for enhanced few-shot object detection

Few-shot learning has attracted significant scientific interest in the past decade due to its applicability to visual tasks with a natural long-tailed distribution such as object detection. This paper introduces a novel and flexible few-shot object detection approach which can be adapted effortlessly to any candidate-based object detection frame-work. In particular, our proposed <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{kFEW}$</tex> component leverages a kNN retrieval technique over the regions of interest space to build both a class-distribution and a weighted aggregated embedding conditioned by the recovered neighbours. The obtained kNN feature representation is used to drive the training process without any additional trainable parameters as well as during inference time by steering the assumed confidence and the predicted box coordinates of the detection model. We perform extensive experiments and ablation studies on MS COCO and Pascal VOC proving its efficiency and state-of-the-art results (by a margin of 2.3 mAP points on MS COCO and by a margin of 2.5 mAP points on Pascal VOC) in the context of few-shot-object detection. Additionally, we demonstrate its versatility and ease-of-integration aspect by incorporating over competitive few-shot object detection methods and providing superior results.
Can Machines Learn to Map Creative Videos to Marketing Campaigns?

The demand for accurate estimation of marketing's incremental effect is rapidly increasing to enable marketers make informed decisions on their ad investment. The process of admapping links an ad shown to consumers on the fixed marketing channels (Linear TV, Digital, Social) to a marketing creative video. Thus, an accurate admapping, which is a special case of video copy detection, is a cornerstone of ensuring exposure of ad is linked to the correct creative and marketing campaign and hence precise marketing effect measurement. With each campaign having tens of creatives and each country (marketplace) having tens of marketing campaigns each week, the current process of human annotation of hundreds of creatives requires over 800+ team's hours annually. Moreover, this manual process causes significant challenges in onboarding new businesses and countries to measurement due to the absence of intelligent model based admapping solution. To solve this problem, we built a machine learning (ML) model that leverages fingerprinting methodology and automatic language identi-fication technology to match each creative to the marketing campaign. In the paper, we present the computing algorithm and implementation details with results from actual campaign dataset. Extensive validation and comparison studies conducted demonstrates improved mapping results with the new proposed method, achieving 87% F1 score and 82% accuracy. To our best knowledge, this is the first model that uses a fusion of visual, audio, language and metadata features for such ML based content mapping solution. The proposed method leads to 90% reduction on the time spent on admapping compared to manual solutions.