Reviewerly/exHarmony huggingface dataset:
Automating ETL and Mining of Ethereum Blockchain Network

The popularity of blockchain technology led to the development of many web platforms with different functionalities. Ethereum, a decentralized, open-source blockchain featuring smart contracts, introduces an interesting ecosystem of human users and autonomous agents (the contracts). It is the most actively used blockchain platform, hosting ether, the second largest cryptocurrency by market capitalization, as its native store of value. The Ethereum blockchain contains a vast amount of user-to-user, user-to-contract, contract-to -user, and contract-to-contract interactions that can be modeled as complex networks. To mine these interactions as graphs through a preferred analytics toolbox, an end-user has to extract, transform, and load (ETL) the required data into the desired network format. However, it is costly and time-consuming to manage the ETL pipeline for the massive and complex blockchain data. To support research in this domain, we develop an end-to-end, automated tool - EtherNet, which performs ETL tasks from a single source of truth (Google BigQuery), and provides graph equivalent representations for visualization and mining on the entire Ethereum blockchain network.
The Origin and Value of Disagreement Among Data Labelers: A Case Study of Individual Differences in Hate Speech Annotation

Human annotated data is the cornerstone of today's artificial intelligence efforts, yet data labeling processes can be complicated and expensive, especially when human labelers disagree with each other. The current work practice is to use majority-voted labels to overrule the disagreement. However, in the subjective data labeling tasks such as hate speech annotation, disagreement among individual labelers can be difficult to resolve. In this paper, we explored why such disagreements occur using a mixed-method approach – including interviews with experts, concept mapping exercises, and self-reporting items – to develop a multidimensional scale for distilling the process of how annotators label a hate speech corpus. We tested this scale with 170 annotators in a hate speech annotation task. Results showed that our scale can reveal facets of individual differences among annotators (e.g., age, personality, etc.), and these facets' relationships to an annotator's final label decision of an instance. We suggest that this work contributes to the understanding of how humans annotate data. The proposed scale can potentially improve the value of the currently discarded minority-vote labels.
Explanations for Network Embedding-Based Link Predictions

Graphs (also called networks) are powerful data abstractions, but they are challenging to work with, as many machine learning methods may not be applied to them directly. Network Embedding (NE) methods resolve this by learning vector representations for the nodes, for subsequent use in downstream machine-learning tasks. Link Prediction is one such important downstream task, used for example in recommender systems. NE methods perform exceedingly well in accuracy for Link Prediction, but predictions following from the embeddings, whose dimensions have no intrinsic meaning, are not straightforward to understand. Explaining why predictions are made can increase trustworthiness, help understand the underlying models and give insight into what features of the network are important in light of the predictions, and answer posed regulatory requirements on the ability to explain machine-learning-based decisions. We study the problem of providing explanations for NE-based link predictions and introduce ExplaiNE, an approach to derive counterfactual explanations by identifying links in the network that explain link predictions. We show how ExplaiNE can be used generically on NE-based methods and consider ExplaiNE in more detail for Conditional Network Embedding, a particularly suitable state-of-art NE method. Extensive experiments demonstrate ExplaiNE’s accuracy and scalability.
An interactive sketch-based CAD interface realizing geometrical and topological editing across multiple objects based on fuzzy logic

With the widespread use of digital pen tablets and touch displays, sketch-based interactive computer-aided design (CAD) user interfaces have attracted considerable attention. In an existing interface called the Sketch Input Tracer (SKIT), however, the editing operation performed via an over-sketching stroke can target only a single geometric object at any given time. To overcome this limitation, in this study, we propose a new general-purpose sketch-based interface developed for use in two-dimensional CAD systems. In the proposed interface, a sketch-based editing operation can simultaneously modify the geometry and topology of multiple geometric objects via over-sketching. The interface was developed based on the fuzzy logic-based strategy of SKIT. With this interface, a user can draw in a creative manner; for example, they can begin with a rough sketch and progressively create a detailed design through repeated over-sketches. The results of experiments conducted demonstrate that experts can control the proposed interactive drawing interface and beginners can habituate to controlling it.
LHPE-nets: A lightweight 2D and 3D human pose estimation model with well-structural deep networks and multi-view pose sample simplification method

The cross-view 3D human pose estimation model has made significant progress, it better completed the task of human joint positioning and skeleton modeling in 3D through multi-view fusion method. The multi-view 2D pose estimation part of this model is very important, but its training cost is also very high. It uses some deep learning networks to generate heatmaps for each view. Therefore, in this article, we tested some new deep learning networks for pose estimation tasks. These deep networks include Mobilenetv2, Mobilenetv3, Efficientnetv2 and Resnet. Then, based on the performance and drawbacks of these networks, we built multiple deep learning networks with better performance. We call our network in this article LHPE-nets, which mainly includes Low-Span network and RDNS network. LHPE-nets uses a network structure with evenly distributed channels, inverted residuals, external residual blocks and a framework for processing small-resolution samples to achieve training saturation faster. And we also designed a static pose sample simplification method for 3D pose data. It implemented low-cost sample storage, and it was also convenient for models to read these samples. In the experiment, we used several recent models and two public estimation indicators. The experimental results show the superiority of this work in fast start-up and network lightweight, it is about 1-5 epochs faster than the Resnet-34 during training. And they also show the accuracy improvement of this work in estimating different joints, the estimated performance of approximately 60% of the joints is improved. Its performance in the overall human pose estimation exceeds other networks by more than 7mm. The experiment analyzes the network size, fast start-up and the performance in 2D and 3D pose estimation of the model in this paper in detail. Compared with other pose estimation models, its performance has also reached a higher level of application.
Underwater U-Net: Deep Learning with U-Net for Visual Underwater Moving Object detection

Underwater object detection is a prevalent way of detecting the movements of objects in the video which has created a dynamic variation. The nature of the problem is complex due to many factors including camera jitter, high haze, dynamic background, absorbing, scattering, and attenuation of light rays in the water medium. It is found that U-Net architecture is popular in image segmentation but not explored for underwater object detection. In this article, a unique attempt is made the first time to use a modified U-Net architecture with ResNet encoders to detect underwater objects. The proposed object detection scheme relies on the principle of background subtraction or local change detection, where a robust background is constructed by taking multiple image frames of a sequence in deeper high dimensional space. The proposed scheme is tested on four different databases and for page constraint, the results are reported here on UNICT underwater database. The objective evaluation of the proposed scheme is carried out using four different performance evaluation measures: precision, recall, F-measures, and similarity. Both the visual and objective evaluation corroborates our findings.
Learning scene-adaptive pseudo annotations for pedestrian detection in semi-supervised scenarios

Sufficient labeled training data may not be available for pedestrian detection in many real-world scenes. Semi-supervised settings naturally apply for the case where an adequate number of images are collected in a target scene but only a small proportion of them can be manually annotated. A common strategy is to adopt a detector trained on a well-established dataset (source data) or the limited annotated data to pseudo-annotate unannotated images. However, the domain gap and the lack of supervision in the target scene may lead to low-quality pseudo annotations. In this paper, we propose a Scene-adaptive Pseudo Annotation (SaPA) approach, which aims at exploiting two types of training data: source data providing sufficient supervision and unannotated target data offering domain-specific information. To utilize the source data, an Annotation Network (AnnNet) competes with a domain discriminator to learn domain-invariant features. To exploit the unannotated data, we temporally aggregate the parameters of AnnNet to build a more robust network, which is able to provide training goals for AnnNet. This new approach improves the generalization performance of AnnNet, which eventually leads to high-quality pseudo annotations to the unannotated data. Both manual and pseudo annotations are leveraged to train a more precise and scene-specific detector. We perform extensive experiments on multiple benchmarks to verify the effectiveness and superiority of SaPA.
Hybrid Evolutionary Technique for Contrast Enhancement of Color Images

Image contrast enhancement is an important task in image processing, which intends to restore the true image from degraded images. The images may be degraded due to noise or blur, and image contrast enhancement performs tasks like deblurring, denoising, etc. Digital images are very essential and important part of our everyday life. The enhancement of contrast is an effective step in image processing as well as in computer vision applications. The histogram equalization technique is used to enhance the contrast of the image, but the histogram equalization technique is not popular due to the artificial effect, immense brightness change, and over enhancement. In this paper, we have proposed a hybrid technique for image contrast enhancement based on an evolutionary algorithm. The proposed technique has been applied to many low-contrast images, and the performance has been compared with the Cuckoo search technique, artificial bee colony technique, and other similar techniques. The nonlinear models are well-optimized with the aid of bio-inspired optimization algorithms to operate adaptively with the noise and blurring models. The proposed approach has shown adequate image enhancement with average results of 26.11 as PSNR score, 620.43 MSE, 24.85 RMSE, 0.5943 UQI, 205.44 MAE, 0.4445 NAE, 4.2992 entropy, and 3.7053 mutual information. Hence, the developed algorithm will be a good compromise to operate between contrast enhancement and denoising techniques.
An approach to Open-Source Software License Management using Blockchain-based Smart-Contracts

Open source software is increasingly being used by various organizations. Open source licenses such as GPL, MIT, Apache, Mozilla, BSD, etc have varying terms and conditions for usage, modification, and distribution. Given the availability of source code for open source software, it is difficult to scrutinize adherence of licenses and more often than not it is left to the good faith of organizations and individuals. Violation of licensing terms knowingly or unknowingly can lead to copyright, ethical, compliance and security issues. In this paper, we propose a blockchain based approach in managing open source software licenses by enforcing certain practices to improve awareness and increase transparency, thus minimizing the possibility of violations. We present a solution design that relies on the use of 4 modules: InterPlanetary File System (IPFS), Smart contracts, Transaction manager (Meta-Mask) and a Permissioned Blockchain to enforce the conformance of licenses.
Rethinking maximum-margin softmax for adversarial robustness

Learning discriminative features with adversarial behaviors can be extremely challenging to build a robust learning model. This is partly evidenced by the difficulties in training robust maximum-margin models (e.g., ArcFace and CosFace) that cannot discriminate decision boundaries between perturbed samples perfectly. One potential approach is to design a loss function that achieves robust generalization by learning high-density features in the latent space to discriminate between adversarial and legitimate samples effectively. Therefore, we propose an Ensemble Maximum-Margin Softmax (EMMS) method to construct a robust generalization that yields reliable models. Specifically, EMMS is designed to address the limitation in maximum-margin methods and induce high-density discriminative features for clean and adversarial settings. The empirical experiments using the CIFAR and SVHN datasets show that EMMS is more robust in terms of accuracy and error rates than other peer techniques. The outcomes suggest that EMMS could improve the robustness of the model compared with ArcFace and CosFace under various types of attacks.
Data Extraction Method for Industrial Data Matrix Codes Based on Local Adjacent Modules Structure

A 2D barcode is a reliable way to provide lifetime traceability of parts that are exposed to harsh environments. However, there are considerable challenges in adopting mobile cameras to read symbols directly marked on metal surfaces. Images captured by mobile cameras are usually of low quality with poor contrast due to the reflective surface of 2D barcode symbols. To deal with this problem, a novel laser-marked Data Matrix symbols reading method based on deep learning is proposed for mobile phone captured images. Utilizing the barcode module features, we train different convolutional neural network (CNN) models to learn the colors of two adjacent modules of a Data Matrix symbol. Depending on whether the colors of the two adjacent modules are the same or not, an edge image is transformed from a square grid, which is the same size as the barcode. A correction method based on the KM algorithm is used to get a corrected edge image, which helps to reconstruct the final barcode image. Experiments are carried out on our database, and the results show that the proposed algorithm outperforms in high accuracy of barcode recognition.
On the correction of errors in English grammar by deep learning

Abstract Using computer programs to correct English grammar can improve the efficiency of English grammar correction, improve the effect of error correction, and reduce the workload of manual error correction. In order to deal with and solve the problem of loss evaluation mismatch in the current mainstream machine translation, this study proposes the application of the deep learning method to propose an algorithm model with high error correction performance. Therefore, the framework of confrontation learning network is introduced to continuously improve the optimization model parameters through the confrontation training of discriminator and generator. At the same time, convolutional neural network is introduced to improve the algorithm training effect, which can make the correction sentences generated by the model generator better in confrontation. In order to verify the performance of the algorithm model, P -value, R -value, F 0.5 -value, and MRR-value were selected for the comprehensive evaluation of the model performance index. The simulation results of the CoNLL-2014 test set and Lang-8 test set show that the proposed algorithm model has significant performance improvement compared with the traditional transformer method and can correct the fluency of sentences. It has good application values.
Mathematical Neural Networks

ANNs succeed in several tasks for real scenarios due to their high learning abilities. This paper focuses on theoretical aspects of ANNs to enhance the capacity of implementing those modifications that make ANNs absorb the defining features of each scenario. This work may be also encompassed within the trend devoted to providing mathematical explanations of ANN performance, with special attention to activation functions. The base algorithm has been mathematically decoded to analyse the required features of activation functions regarding their impact on the training process and on the applicability of the Universal Approximation Theorem. Particularly, significant new results to identify those activation functions which undergo some usual failings (gradient preserving) are presented here. This is the first paper—to the best of the author’s knowledge—that stresses the role of injectivity for activation functions, which has received scant attention in literature but has great incidence on the ANN performance. In this line, a characterization of injective activation functions has been provided related to monotonic functions which satisfy the classical contractive condition as a particular case of Lipschitz functions. A summary table on these is also provided, targeted at documenting how to select the best activation function for each situation.
An Edge-Enabled Guidance Algorithm for Autonomous Robotic Navigation

In this paper, we develop an algorithm for a mobile robot to navigate from one point to another in the geodetic coordinate system, while avoiding obstacles, using edge computing devices and navigational sensors. This enables the robot to detect changes in its position, respond, and control its mechanical behavior towards following specified path points to reach the designated location on the earth’s surface. Several experiments were performed in an open field to evaluate the efficiency of the algorithm and the accuracy of the sensors. Results show that the guidance control algorithm for autonomous robotic navigation using edge computing devices is practicable, cost-saving, and reliable.
Diversified Position Update Equation-Based SSA with Refreshing-Gap Strategy for global optimization

This study, incorporating three improvements into the structure of the original SSA algorithm to increase its exploration and exploitation capabilities, suggests an SSA variant algorithm named ”Diversified Position Update Equation-Based SSA with Refreshing-Gap Strategy (DSSA-R)”. Unlike the original SSA, DSSA-R accepts half of the population as the leader instead of using a single leader salp strategy. Nevertheless, the suggested algorithm includes two different equations to update the positions of the leader salps and follower salps. Furthermore, Refreshing Gap strategy was included in DSSA-R to avoid the algorithm from being stuck in the local optimum. The final improvement made on the original SSA was the addition of ”Linear Population Reduction Strategy” to strengthen the exploitation aspect of the algorithm. The performance of the DSSA-R algorithm was tested using the functions of the CEC 2014 and CEC 2015 benchmark suites and compared with five SSA variants and four state-of-the-art algorithms. Test results show that DSSA-R obtained better and competitive results compared to the algorithms it was compared with.
A Learnable Image Compression Scheme for Synthetic Aperture Sonar Imagery

Synthetic aperture sonar (SAS) is an imaging modality which produces high and constant resolution images of the seafloor. These sonars are often mounted to a unmanned underwater vehicle (UUV) to autonomously collect imagery of a prescribed survey area. While a survey is underway, UUV communications back to the operator are often limited due to the use of a low-bandwidth acoustic communications (ACOMMS) channel. Because of this, high-quality SAS imagery is rarely sent over this link due to the lack of an efficient compression scheme to send such information. Creating an efficient SAS image compression scheme provides at least two operational benefits: (1) image chips beamformed and tagged by onboard processing algorithms can be quickly communicated to operators while a survey is ongoing, and (2) cooperative UUVs can exchange salient image chips among themselves to reconcile position ambiguity and obtain a shared reference frame. In this work we propose a learned image compression scheme for SAS imagery using deep neural networks (DNNs). DNNs have already been applied to the image compression problem but almost exclusively for optical imagery. We highlight some important differences between SAS imagery and optical imagery which prevents the simple application of off-the-shelf (OTS) methods like JPEG and WebP to SAS imagery. We propose an image compression scheme which specifically addresses the domain-specific properties of SAS imagery to obtain useful image compression performance on a real-world SAS dataset. We show that we can reduce the bitrate by up to thirty-five percent while still maintaining the same perceptual image quality as OTS codecs.
A Novel High-Order Cluster-GCN-Based Approach for Service Recommendation

When exploring high-order neighbors for embedding learning, data sparsity problems in service recommendation system can be compensated via Graph Convolutional Network (GCN). However, the performance of GCN will deteriorate when stacking more layers, namely, over-smoothing problem. Though LightGCN and LR-GCN can alleviate over-smoothing and achieve state-of-the-art performance, all users with dissimilar preferences become similar and the services become homogeneous, introducing noise information in exploration high-order graph convolution. Thus, we argue that the loss of uniqueness of all nodes is the cause of over-smoothing problems in high-order graph convolution. To solve the above problems, we propose to use graph clustering algorithm to cluster user-service graph. Moreover, this node enhancement technique in our model can further facilitate systems to learn more information from nearby neighbors. The experimental results confirm that the proposed algorithm outperforms most baseline algorithms, achieving state-of-the-art performance.
Deep Learning Side-Channel Attacks Against Lightweight SCA Countermeasure RSM-AES

Recently, deep learning side-channel attacks (DL-SCA) have been actively studied. The attacks against a software-implemented AES with lightweight countermeasures such as misalignment and masking have already been successfully demonstrated by utilizing simple DL-SCA. On the other hand, it is not clarified how much performance is exhibited on hardware-implemented AES circuits. The XORed model using register transition has been used for simple DL-SCA against hardware-implemented AES circuits. However, partial keys on 0, 4, 8, and 12 bytes cannot be revealed by simply applying the XORed model because of data imbalance problems. In the previous study, the ‘mix XORed model’ method had been proposed, and partial keys on 0, 4, 8, and 12 bytes are successfully attacked by utilizing training data of other key bytes. This method can reveal all partial keys on unprotected AES hardware; however, it is difficult to reveal all keys on RSM (Rotating S-Box Masking) AES hardware even with 100,000 traces. In this paper, we experimentally applied the ‘random XORed model’ where waveforms during processing with random keys are used as the training dataset in order to resolve the data imbalance problem on partial keys on 0, 4, 8, and 12 bytes. In our experiments, we demonstrated that all partial keys are successfully revealed against unprotected AES with 600 traces and RSM-AES circuits with 45,000 traces.