Reviewerly/exHarmony huggingface dataset:
An Approach for Character Recognition in Piston Cavity with Faster R-CNN and Prior Knowledge Library of Character Sequences

In the manufacturing process of piston, most of the piston cavities are printed with different character sequences to describe the specifications of piston. It is labor-consuming and inefficient to read the piston cavity character sequences manually. Although scholars have done a lot of researches in the field of industrial character recognition, there are few researches on piston cavity character recognition. A piston cavity character recognition method based on Faster R-CNN and priori knowledge library of character sequences is presented. First, we design a ring light source and an imaging device for the piston cavity based on the characters of the piston cavity protruding upward and the texture of the metal being easily reflective. Second, we use the character images in piston cavity collected by the imaging device to make character dataset. Third, according to the noisy background of the piston cavity image, Gaussian filtering and morphological operations were used to obtain a clean background image of the piston cavity. Fourth, use the Faster R-CNN training dataset to get the character recognition model, and then use the character type and position information detected by the recognition model to form a character sequence. Fifth, according to the highly similar characteristics of the character sequences of the same piston model, a character sequences prior knowledge library is constructed to correct the recognition results of Faster R-CNN. The experimental results show that the accuracy rate of the character sequences detected by the character recognition model is 95.5%. And when combined with the prior library of character sequences, the accuracy rate of the character sequences is 99%.
Signal detection algorithms for interferometric sensors with harmonic phase modulation: evaluation of additive noise effects

Interferometric techniques are widely used for interrogation of various optical sensors. Techniques utilizing harmonic auxiliary phase modulation are of special interest from a practical point of view, as they do not require a large bandwidth and dynamic range of a phase modulator, yet are capable of accurately demodulating target phase signals when jointed with proper demodulation algorithms. The current paper presents a universal theoretical apparatus aimed at estimating the influence of the noise of an interference signal on noise of the demodulated phase. Two demodulation algorithms developed in our previously published paper [ Appl. Opt. 56 , 7960 ( 2017 ) APOPAI 0003-6935 10.1364/AO.56.007960 ] are analyzed in terms of their susceptibility to additive noise. By comparing the obtained results with the Cramer–Rao bound, optimal conditions for these algorithms are found.
Simplifying continuous-time quantum walks on dynamic graphs

A continuous-time quantum walk on a dynamic graph evolves by Schrödinger's equation with a sequence of Hamiltonians encoding the edges of the graph. This process is universal for quantum computing, but in general, the dynamic graph that implements a quantum circuit can be quite complicated. In this paper, we give six scenarios under which a dynamic graph can be simplified, and they exploit commuting graphs, identical graphs, perfect state transfer, complementary graphs, isolated vertices, and uniform mixing on the hypercube. As examples, we simplify dynamic graphs, in some instances allowing single-qubit gates to be implemented in parallel.
Study of robust facial recognition under occlusion using different techniques

This paper represents the different techniques used for the study of robust facial recognition. The need of facial recognition is expanding very rapidly in current technologies as it is possible to identify a humane face features through different digital mediums. Although, facial recognition is not done achieved easily in the real-world situation as most of the time the human face goes through various occlusions making hard for the system to complete the identification process. The sole purpose of this article is to compare the techniques used and identify the most accurate and efficient one from all of them. Robust facial recognition is a task of doing recognition under uncertain environments and features. The main aim of the robust facial recognition is to analyze and identify the face images where the picture proposes difficult viewpoints, angles, poses, illumination, noises and expressions. The techniques studied for this article are Local Binary Patterns, HOG features, Occlusion-adaptive Deep Networks (ODN), Robust Principal Component Analysis (RPCA), Progressive Convolutional Neural Network and Region Attention Networks (RAN). In this article, a table is created summarizing the experiments and results of the techniques studied.
Characterizing the I/O Pipeline in the Deployment of CNNs on Commercial Accelerators

Commercial AI accelerators are gaining popularity because of their high energy efficiency for the inference of deep neural networks (DNNs). How to benchmark them remains hot research topic. Existing characterizing researches mainly focus on the hardware execution latency, without considering the pre- or post-processing and the copy-in or copy-out overhead, which is important during the end-to-end inference. Motivated by this, we model the end-to-end inference phase of commercial DNN accelerators with a five-stage I/O pipeline, including preprocessing, copy-in, hardware execution, copy-out, and postprocessing stages. We further investigate the involving factors in each stage and implement the whole I/O pipeline on three different hardware platforms, covering the emerging DNN accelerators and the traditional CPUs and GPUs. With six DNNs from real-world computer vision applications, we take a deep dive into the DNN inference pipeline and quantify the influence of each stage on the throughput. Our experimental results demonstrate the effect of this I/O pipeline and highlight the necessity of end-to-end evaluation. Moreover, the I/O pipeline we implemented is a flexible benchmarking tool, which can help users conduct an in-depth end-to-end evaluation of DNN inference phase.
A Comprehensive Survey for Detection and Prevention of SQL Injection

This project aims to stop SQL injection attacks and make the database safer. This system is online, and there is no need for implementation. This can be accessed from any location via the internet. This framework uses SQL injection protection to keep the database and data safe. It will make particular emphasis on encrypting Credit card data using AES (Advanced Encryption Standard) technique. The shop secures payment so that everything will be secured. The user pay card data is then stored in a database. The device also stores user information in an encrypted form through the use of AES encryption. The framework is designed to avoid SQL injection activities that can break into the database and run it.
Implementing Game Strategies Based on Reinforcement Learning

Artificial intelligence (AI) technology such as reinforcement learning is increasingly used in playing game in recent years. A deep reinforcement learning model was used to play the game Flappy Bird. This paper aimed to let the computer play a simple game and get the corresponding data for AI learning. Game image was sequentially scaled, grayed, and adjusted for brightness. Before the current frame entered a state, the multi-dimensional image data of several frames of image superposition and combination was processed. Deep Q Network algorithm realized the best action prediction of the game execution in a specific game state, and successfully converted a game decision problem into the classification and recognition problem of instant multi-dimensional images and solved it with a convolutional neural network. After analysis, computer players controlled by deep neural networks had better results than human players. This experiment was a model combined between a deep neural network model and reinforcement learning, and could be applied in other games.
An event driven framework for smart contract execution

Blockchain-based smart contract platforms have traditionally employed the transaction-driven execution model. This paper presents an alternate framework for blockchain-based smart contract execution called EDSC. Our platform design presents a novel approach to tackle the scalability and performance challenges facing the smart contract ecosystem. We base EDSC's design on the Ethereum template, and it can be readily implemented for other existing smart contract platforms. To evaluate our design, we perform an experimental implementation using the Ethereum client. Our experiments with performance modeling show, on average, a 2.2 to 4.6 times reduced total latency of event-triggered smart contracts, demonstrating the effectiveness of the design in supporting time-sensitive applications. Additionally, we comment on the design's potential security aspects and demonstrate its utility by discussing potential use cases.
How the Discrete Hirschman Transform Inherits its Eigenstructure from the DFT

Following the earlier work, the discrete Hirschman transform (DHT) structure is extended to other transforms: the cosine, sine, Walsh-Hadamard and the wavelet transforms. The eigenstructure of the discrete Hirschman transforms are derived, and in fact we show that they are inherited directly from the DFT eigenstructure. Two properties of the discrete Hirschman transforms, invertibility and unitarity, are proven.
FASSD-Net Model for Person Semantic Segmentation

This paper proposes the use of the FASSD-Net model for semantic segmentation of human silhouettes, these silhouettes can later be used in various applications that require specific characteristics of human interaction observed in video sequences for the understanding of human activities or for human identification. These applications are classified as high-level task semantic understanding. Since semantic segmentation is presented as one solution for human silhouette extraction, it is concluded that convolutional neural networks (CNN) have a clear advantage over traditional methods for computer vision, based on their ability to learn the representations of appropriate characteristics for the task of segmentation. In this work, the FASSD-Net model is used as a novel proposal that promises real-time segmentation in high-resolution images exceeding 20 FPS. To evaluate the proposed scheme, we use the Cityscapes database, which consists of sundry scenarios that represent human interaction with its environment (these scenarios show the semantic segmentation of people, difficult to solve, that favors the evaluation of our proposal), To adapt the FASSD-Net model to human silhouette semantic segmentation, the indexes of the 19 classes traditionally proposed for Cityscapes were modified, leaving only two labels: One for the class of interest labeled as person and one for the background. The Cityscapes database includes the category “human” composed for “rider” and “person” classes, in which the rider class contains incomplete human silhouettes due to self-occlusions for the activity or transport used. For this reason, we only train the model using the person class rather than human category. The implementation of the FASSD-Net model with only two classes shows promising results in both a qualitative and quantitative manner for the segmentation of human silhouettes.
Photovoltaic Image Registration Based on Feature Matching via Guided Spatial Consensus

Due to the high similarity of photovoltaic images, the difficulty of registering photovoltaic images increases while the accuracy requirement for registering images is high. This article proposed a guided spatial consensus registration algorithm based on local structural similarity constraint. First, local similarity structural constraints are established based on the characteristics of the neighborhood structure among adjacent matching feature points. Second, outliers are eliminated based on the proposed similarity structural constraints to obtain a high-precision matching feature set among a strictly constrained initial matching feature set. Third, the corresponding geometric parameter of the high-precision matching feature set is used as guided information for the selection of the final inlier set from a weakly constrained initial matching feature set. Finally, the proposed method is validated by photovoltaic images with brightness and geometric differences. The experimental results demonstrate the robustness and efficiency of the proposed algorithm in the application of photovoltaic images.
Detecting Highlighted Video Clips Through Emotion-Enhanced Audio-Visual Cues

Recent years have witnessed the growing research interests in video highlight detection. Existing studies mainly focus on detecting highlights in user-generated videos with simple topics based on visual content. However, relying solely on visual features limits the ability of conventional methods to capture highlights for videos with more complicated semantics, like movies. Therefore, we propose to mine the emotional information in video sounds to enhance highlight detection. Specifically, we design a novel emotion-enhanced framework with multi-stage fusion to detect highlights for complex videos. Along this line, we first extract multi-grained features from the audio waves. Then, the tailored-designed intra-modal fusion is applied on audio features to obtain emotional representation. Furthermore, the cross-modal fusion is developed to generate comprehensive representation of clip by merging audio emotional representations and visual features. This representation can be leveraged for predicting highlight probability. Finally, extensive experiments on real-world datasets demonstrate the effectiveness of our method.
Anomaly Detection of Dust Removal System Through Gradient Boosting Decision Tree Algorithm

In the process of modern industrial production, the breakdown of industrial equipment will bring serious economic loss, making the diagnosis of equipment a research hotspot. Among numerous equipment, the dust removal system contains many sub-equipment and parameters, which make its fault diagnosis a challenging problem. This paper introduces a solution to the anomaly detection problem of dust removal fans based on the improved K-means combined with the gradient boosting decision tree (GBDT) model. This study uses K-means to divide the obtained original data into different classes, then utilizes the model to complete the classification and prediction tasks for anomaly detection. It was found that the model used is more suitable for the fault diagnosis of the dust removal fan.
Privacy-Preserving Distributed Learning with Nonsmooth Objective Functions

This paper develops a fully distributed differentially-private learning algorithm based on the alternating direction method of multipliers (ADMM) to solve nonsmooth optimization problems. We employ an approximation of the augmented Lagrangian to handle nonsmooth objective functions. Furthermore, we perturb the primal update at each agent with a time-varying Gaussian noise with decreasing variance to provide zero-concentrated differential privacy. The developed algorithm has competitive privacy-accuracy trade-off and applies to nonsmooth and non necessarily strongly convex problems. Convergence and privacy-preserving properties are confirmed via both theoretical analysis and simulations.
Securing Cloud Storage by Remote Data Integrity Check with Secured Key Generation

In recent decades, a number of protocols for Remote data integrity checking (RDIC) have been proposed. Identity (ID) based RDIC protocols are constructed to guarantee cloud data integrity and data privacy. The known protocols for RDIC always assume that the Private key generator (PKG) is a trusted one, but in real-world applications by corrupt PKG, malicious Cloud server (CS) can easily cheat the third party auditor that the data owner's outsourced data are kept safe through the data has been deleted or altered. In this paper, we explore the novel model of RDIC with untrusted PKG and malicious CS, by employing the partial key method and Authentication, authorization, accounting (AAA) service. We construct a new ID-based RDIC, which provides the ID revocation and key updation. The experimental evaluations show that our scheme is more efficient than known ones.
<scp>Scale‐Adaptive</scp> Kernel Correlation Filter with Maximum Posterior Probability Estimation and Combined Features for Visual Target Tracking

Although the correlation filtering tracker for visual target tracking has achieved excellent results in both accuracy and robustness, there are still some problems yet to be solved. Obtaining stable scale estimation using traditional trackers is a challenging problem in visual target tracking, and many trackers fail to handle scale change in complex video sequences. In order to solve the problems of scale change, partial occlusion and geometric deformation for target tracking effectively, a new tracker based on kernel correlation filtering is developed in our study. The tracker obtained with maximum posterior probability method has scale adaptive ability and can deal with scale change to improve the tracking ability. In addition, the tracker further enhances the ability to deal with illumination variation, geometric deformation and occlusion by fusing the adaptive color naming feature and the histogram of oriented gradient feature as well. The VOT‐2018 which has 50 video sequences is used as the benchmark data set in this work and the simulation evaluation on this data set have shown that the proposed tracker has achieved stable tracking results in some challenging scenarios and can achieve better tracking performance than other trackers. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Compact Bit-Parallel Systolic Multiplier Over GF(2<i><sup>m</sup></i>)

This article presents a compact and efficient bit-parallel systolic array structure for multiplication over the extended binary field, GF(2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> ). The systolic array has a regular arrangement with local connections, making it more suitable for VLSI implementations. Also, it has the merits of having hardware complexity of order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (m) that distinguishes it from the previously reported bit-parallel designs having hardware complexity of order <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">O</i> (m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The achieved results exhibited that the suggested parallel architecture realizes a significant reduction in hardware complexity and the area-delay complexity over the competitor architectures previously published in the literature. Therefore, it is more suitable for usage in constrained hardware environments, having more restrictions on space, such as portable devices and smart cards.
Intents Categorization for Chatbot Development Using Recurrent Neural Network (RNN) Learning

In the chatbot application, the intent is a set of sentences representing specific user intention while interacting with the chatbot. A set of sentences with the same purpose should be categorized precisely to provide appropriate answers. For humans, understanding the meaning of sentence variations with the same intention is straightforward. But for chatbots, it requires a special technique. This study aims to develop a conversation intent categorization model on smart chatbots with a deep learning algorithm. Hence, the variation of sentence expressions can be understood by chatbots more accurately. The result of using RNN is promising as it reaches 81% accuracy.
Tangibility As Technology

Property law has traditionally relied on tangible boundaries to delineate legal thinghood and to inform the bounds of in rem rights and duties. Unfortunately, property doctrines have fossilized around tangibility, causing fragmentation in the legal treatment of digital assets. In the United States, for example, cryptocurrencies and non-fungible tokens (NFTs) may simultaneously be classified as commodities, securities, currencies, assets, or not property at all, depending on the jurisdiction, domain, or specific asset in question. This fragmented system of overlapping legal treatments increases the information cost of using digital assets, decreases efficiency, and ultimately hinders future innovation.In this piece, I propose a unified and tech neutral approach to legal thinghood, providing a theoretically coherent and robust way to increase property law’s resilience in adapting to future technologies. Specifically, I deconstruct the conceptual purpose of tangibility in traditional doctrines of legal thinghood, uncovering its role as a technology (i.e., a tool) in property law to delineate rights. From this insight, I derive a coherent doctrinal test for distinguishing between digital assets that fulfill all conceptual requisites to be legal things and assets that do not. By doing so, I conclude that the traditional ontological categories of property law, such as choses in possession, are sufficiently robust to incorporate new and evolving digital assets. This tech-neutral approach paves the way toward an elegant and efficient legal treatment of digital assets and digital resource management in the twenty first century.
The Opacity of ‘Transparent’ Bitcoin Data

Transparency of data recorded on distributed ledgers has been hailed as a key benefit of blockchains, such as Bitcoin. Our examination of 389 million Bitcoin wallet addresses involving 183 million unique users shows that the way the “Notebreaker” wallet mechanism handles transactions introduces remarkable opacity into otherwise “transparent” data. Utilizing a novel algorithm that achieves 90% accuracy at separating “economic value” from “security value” in Notebreaker blockchains, we show that transaction volumes are inflated 7-8 times and transaction fees are inflated 7-15 times what is commonly believed when compared to economic transfer undertaken. A common heuristic for unique adopter counts – address counts – is also overstated. Our study identifies a key weakness in public Bitcoin data and the danger of extrapolating adoption levels of blockchains utilizing Notebreaker.
Spatial-Temporal Integration Network with Self-Guidance for Robust Video Deraining

Recently, video deraining has become a research focus. Network-based approaches are continuously showing extrusive performance. However, they lack precise control over the motion consistency in temporal information and characterize spatial distribution, so that their results are unsatisfying, especially in some real-world scenarios. To settle them, we develop a spatial-temporal integration network with self-guidance. It contains flow-induced alignment, self-guidance generation, and spatial-temporal integration modules. The alignment module not only preliminarily removes rain to provide more effective temporal correlation but also accurately keeps motion consistency between frames. The self-guidance map characterizes the pixel-level spatial distribution for the target to avoid injuring the background. Finally, we concatenate adjacent aligned frames, self-guidance map, and original current rain frame into the integration module to progressively fuse them in a coarse-to-fine way. Extensive evaluations demonstrate our superiority against other state-of-the-art methods qualitatively and quantitatively.