Reviewerly/exHarmony huggingface dataset:
Confidence-Aware Object Detection Based on MobileNetv2 for Autonomous Driving

Object detection is an indispensable part of autonomous driving. It is the basis of other high-level applications. For example, autonomous vehicles need to use the object detection results to navigate and avoid obstacles. In this paper, we propose a multi-scale MobileNeck module and an algorithm to improve the performance of an object detection model by outputting a series of Gaussian parameters. These Gaussian parameters can be used to predict both the locations of detected objects and the localization confidences. Based on the above two methods, a new confidence-aware Mobile Detection (MobileDet) model is proposed. The MobileNeck module and loss function are easy to conduct and integrate with Generalized-IoU (GIoU) metrics with slight changes in the code. We test the proposed model on the KITTI and VOC datasets. The mean Average Precision (mAP) is improved by 3.8 on the KITTI dataset and 2.9 on the VOC dataset with less resource consumption.
Socio-economic impact of Blockchain utilization on Digital certificates

This research is an effective digital certificate publishing activity by utilizing blockchain technology. By implementing decentralized systems and cryptography owned by the blockchain, it will potentially enlarge the educational progress space in Indonesia. The weakness of the current running system, lies in the difficulty of identifying the validity of a certificate issued by an educational institution. Therefore, the advantages that blockchain has, able to assist an educational institution, in establishing a widely accessible certification system infrastructure, to support the transparency and accountability of an educational institution, in identifying the validity of the certificate. Blockcert as one of MIT's tools, while acting as a barometer, is used to perform a series of activities in publishing activities and the implementation of official documents that are designed based on a technology commonly referred to as Blockchain. The research conducted will demonstrate that a decentralized consensus will be able to provide good distribution of information that can include all certificate deployment activities.
Multi-Scale Person Localization With Multi-Stage Deep Sequential Framework

Person detection in real videos and images is a classical research problem in computer vision.Person detection is a nontrivial problem that offers many challenges due to several nuisances that commonly observed in natural videos.Among these, scale is the main challenging problem in various object detection tasks.To solve the scale problem, we propose a framework that estimates the scales of person's heads, as we argue that head is the only visible part in complex scenes.we propose a head detection framework that explicitly handles head scales.The framework consists of two sequential networks: (1) scale estimation network (SENet) and ( 2) head detection network.SENet predicts the distribution of scales from the input image in the form of histogram.Then the scale histogram adjust anchor scale set of region proposal network that generates object proposals.These objects proposals are then classified into two classes, that is, head and background by the detection network.We evaluate proposed framework on three challenging benchmark datasets.Experiment results show that proposed framework achieves state-of-the-art performance.
A new food chain: Adoption and policy implications to blockchain use in agri‐food industries

Abstract Legislation and regulation proposed to study, fund, and govern blockchain use is emerging among both US federal and state governments. These regulatory requirements, however, are not fully consistent across jurisdictions, which may add further challenge to the adoption of this technology by agri‐food system firms. This study compiles and provides a descriptive overview of legislation and regulations related to blockchain technology. Implications of the current regulatory approach on the adoption of blockchain on the US agri‐food system, and specifically of the wider adoption of distributed ledger technologies on food safety and market access of smaller scale farm operations, are considered.
Portfolio Optimalization on Digital Currency Market

Virtual currency represents a specific technological innovation on financial markets. Bitcoin and other cryptocurrencies are popular alternatives to traditional cash and investment. We indicate a research gap in the literature review. We find out that current research focused rarely on portfolio diversification using bibliographic analysis in VOSviewer. We think that portfolio diversification is extremely important on the crypto market for most investors because virtual currencies are very risky compared to traditional assets. The primary aim is to construct an optimal portfolio consisting of several cryptocurrencies without traditional assets using a modern theory portfolio. The total sample consists of 16 virtual currencies from 1 October 2017 to 13 January 2020. We mainly obtain historical data on the daily close price of cryptocurrencies from Yahoo Finance. The results show that the optimal portfolio using Markowitz approach consists of Cardano, Binance Coin, and Bitcoin. In addition, virtual currencies are moderately Correlated, with the exception of Tether based on correlation analysis. The high correlation is dangerous for cryptocurrency in portfolio diversification. However, Tether is an atypical virtual currency compared to other cryptocurrencies.
Local Area Network Monitoring: The Issue of Broadcast Storm

This article describes the main methods of analysis of local area network (LAN).Detection of broadcast storm is an issue upon such monitoring.Storm is one of the main failure causes of operation of corporate LAN, which can result in property and reputation damage if it is not prevented at the level of monitoring of network resources or at the level of network settings.This work describes in details the tools and mechanisms of revealing, elimination, prevention of storm spreading.The results are presented as formal models reflecting execution of business processes at the enterprise while organizing monitoring (BPMN diagrams) and reflecting system of planning and resource usage upon implementation of respective process (EPC diagrams).The main protocols of LAN monitoring are analyzed, which is an important factor to understand settings of equipment and overall LAN.The obtained results can be applied for designing and development of comprehensive system of failure prediction or monitoring of corporate LAN.
Pseudo Siamese Network for Few-shot Intent Generation

Few-shot intent detection is a challenging task due to the scare annotation problem. In this paper, we propose a Pseudo Siamese Network (PSN) to generate labeled data for few-shot intents and alleviate this problem. PSN consists of two identical subnetworks with the same structure but different weights: an action network and an object network. Each subnetwork is a transformer-based variational autoencoder that tries to model the latent distribution of different components in the sentence. The action network is learned to understand action tokens and the object network focuses on object-related expressions. It provides an interpretable framework for generating an utterance with an action and an object existing in a given intent. Experiments on two real-world datasets show that PSN achieves state-of-the-art performance for the generalized few shot intent detection task.
Quantum variational approach to lattice gauge theory at nonzero density

The simulation of dense fermionic matters is a long-standing problem in lattice gauge theory. One hopeful solution would be the use of quantum computers. In this paper, digital quantum simulation is designed for lattice gauge theory at nonzero density. The quantum variational algorithm is adopted to obtain the ground state at nonzero density. A benchmark test is performed in the lattice Schwinger model.
Causality-in-mean and causality-in-variance among Bitcoin, Litecoin, and Ethereum

Purpose This study aims to examine the volatility spillovers between Bitcoin (BTC), Litecoin (LTC) and Ethereum (ETH) as they are related to structural breaks. Design/methodology/approach This study examines the daily period from August 7, 2015 to July 10, 2018 by conducting causality-in-mean and causality-in-variance tests among cryptocurrencies. Findings The findings showed that there was one-way causality-in-mean from BTC to LTC and ETH, but there was no causality-in-mean from LTC and ETH to BTC. On the other hand, considering the structural breaks included in the variance equations, the estimation results showed that there were short-term causality-in-variance from LTC to BTC and long-term causality-in-variance from BTC to LTC. Originality/value This study fills the gap by contributing in two ways. First, to the best of the authors’ knowledge, this is the first study that used the cross-correlation function (CCF) of causality to explore causality-in-variance among cryptocurrencies. Second, this study considers the structural breaks in variance in the return series.
An empirical study of personalized advertising recommendation based on DBSCAN clustering of sina weibo user-generated content

Today, with the dividend of mobile Internet traffic disappearing, mobile advertising is focusing on powerful algorithms of personalized recommendation. Sina Weibo has abundant advertising spaces and the accuracy of its recommendation directly affects user experience. UGC (user-generated content) is the selected or even original edited content for interaction through users’ subjective interests, which reflects users’ inner interests within a certain period of time. Adaptive DBSCAN clustering is performed on Weibo UGC, and the clustering center represents the user’s interest to a certain extent. By refining the text theme of advertisements to be recommended, the similarity between clustering center and the recommended theme is obtained, and the advertising category with high similarity is recommended to user. The experiment proves that the method is effective.
A General Method For Automatic Discovery of Powerful Interactions In Click-Through Rate Prediction

Modeling powerful interactions is a critical challenge in Click-through rate (CTR) prediction, which is one of the most typical machine learning tasks in personalized advertising and recommender systems. Although developing hand-crafted interactions is effective for a small number of datasets, it generally requires laborious and tedious architecture engineering for extensive scenarios. In recent years, several neural architecture search (NAS) methods have been proposed for designing interactions automatically. However, existing methods only explore limited types and connections of operators for interaction generation, leading to low generalization ability. To address these problems, we propose a more general automated method for building powerful interactions named AutoPI. The main contributions of this paper are as follows: AutoPI adopts a more general search space in which the computational graph is generalized from existing network connections, and the interactive operators in the edges of the graph are extracted from representative hand-crafted works. It allows searching for various powerful feature interactions to produce higher AUC and lower Logloss in a wide variety of applications. Besides, AutoPI utilizes a gradient-based search strategy for exploration with a significantly low computational cost. Experimentally, we evaluate AutoPI on a diverse suite of benchmark datasets, demonstrating the generalizability and efficiency of AutoPI over hand-crafted architectures and state-of-the-art NAS algorithms.
XAlgo: a Design Probe of Explaining Algorithms’ Internal States via Question-Answering

Algorithms often appear as 'black boxes' to non-expert users. While prior work focuses on explainable representations and expert-oriented exploration, we propose and study an interactive approach using question answering to explain deterministic algorithms to non-expert users who need to understand the algorithms' internal states (students learning algorithms, operators monitoring robots, admins troubleshooting network routing). We construct XAlgo—a formal model that first classifies the type of question based on a taxonomy and generates an answer based on a set of rules that extract information from representations of an algorithm's internal states, the pseudocode. A design probe based on an algorithm learning scenario with 18 participants (9 for a Wizard-of-Oz XAlgo and 9 as a control group) reports findings and design implications based on what kinds of questions people ask, how well XAlgo responds, and what remain as challenges to bridge users' gulf of algorithm understanding.
Colexification Networks Encode Affective Meaning

Colexification is a linguistic phenomenon that occurs when multiple concepts are expressed in a language with the same word. Colexification patterns are frequently used to estimate the meaning similarity between words, but the hypothesis that these are related is still missing direct empirical validation at scale. Here, we show for the first time that words linked by colexification patterns capture similar affective meanings. Using pre-existing translation data, we extend colexification databases to cover much longer word lists. We achieve this with an unsupervised method of affective lexicon extension that uses colexification network data to interpolate the affective ratings of words that are not included in the original lexicon. We find positive correlations between network-based estimates and empirical affective ratings, which suggest that colexification networks contain information related to affective meanings. Finally, we compare our network method with state-of-the-art machine learning, trained on a large corpus, and show that our simple linguistics-informed unsupervised algorithm yields comparable performance with high explainability. These results show that it is possible to automatically expand affective norms lexica to cover exhaustive word lists when additional data are available, such as in colexification networks.The online version contains supplementary material available at 10.1007/s42761-021-00033-1.
Scoping review on automatic color equalization algorithm

Digital image processing is at the base of everyday applications aiding humans in several fields, such as underwater monitoring, analysis of cultural heritage drawings, and medical imaging for computer-aided diagnosis. The starting point of all such application regards the image enhancement step. A desirable image enhancement step should simultaneously standardize the illumination in the image set, possibly removing bad or not-uniform illumination effects, and reveal all hidden details. In 2002, a successful perceptual image enhancement model, the automatic color equalization (ACE) algorithm, was proposed, which mimics the color and contrast adjustment of the human visual system (HVS). Given its widespread usage, its correlation with the HVS, and since it is easily implementable, we propose a scoping review to identify and classify the available evidence on ACE, starting from the papers citing the two funding papers on the algorithm. The aim of this work is the identification of what extent and in which ways ACE may have influenced the research in the color imaging field. Thanks to an accurate process of papers tagging, classification, and validation, we provide an overview of the main application domains in which ACE was successfully used and of the different ways in which this algorithm was implemented, modified, used, or compared.
Differential evolution algorithm with population knowledge fusion strategy for image registration

Abstract Image registration is a challenging NP-hard problem within the computer vision field. The differential evolutionary algorithm is a simple and efficient method to find the best among all the possible common parts of images. To improve the efficiency and accuracy of the registration, a knowledge-fusion-based differential evolution algorithm is proposed, which combines segmentation, gradient descent method, and hybrid selection strategy to enhance the exploration ability in the early stage and the exploitation ability in the later stage. The proposed algorithms have been implemented and tested with CEC2013 benchmark and real image data. The experimental results show that the proposed algorithm is superior to the existing algorithms in terms of solution quality, convergence speed, and solution success rate.
Circuit World: A Multiplayer VE for Researching Engineering Learning

Business travel leads to the consumption of large amounts of energy. Circuit World (CW), a Unity virtual environment, uses avatars and realistic simulations of electrical circuits to test the effectiveness of teaching complex engineering tasks without travel. To evaluate CW, a study compares how well participants learn to repair a circuit when trained face-to-face, via Zoom, and within CW wearing an HMD. After training and completion of a knowledge quiz, learners are given a circuit to repair by themselves using a non-immersive desktop version of CW. Participants repair it again two weeks later to test retention. Each study session involves three people: learner, trainer, and researcher. The learner and trainer control avatars and the researcher, invisible, observes over the learner's shoulder. Collected data includes task performance logs from CW, knowledge quiz scores, and, based on video recordings, eye gaze tracking, behavioral coding, and measures of facial synchrony between trainer and learner.
One-Way Functions and Malleability Oracles: Hidden Shift Attacks on Isogeny-Based Protocols

Supersingular isogeny Diffie-Hellman key exchange (SIDH) is a post-quantum protocol based on the presumed hardness of computing an isogeny between two supersingular elliptic curves given some additional torsion point information. Unlike other isogeny-based protocols, SIDH has been widely believed to be immune to subexponential quantum attacks because of the non-commutative structure of the endomorphism rings of supersingular curves. We contradict this commonly believed misconception in this paper. More precisely, we highlight the existence of an abelian group action on the SIDH key space, and we show that for sufficiently unbalanced and overstretched SIDH parameters, this action can be efficiently computed (heuristically) using the torsion point information revealed in the protocol. This reduces the underlying hardness assumption to a hidden shift problem instance which can be solved in quantum subexponential time. We formulate our attack in a new framework allowing the inversion of one-way functions in quantum subexponential time provided a malleability oracle with respect to some commutative group action. This framework unifies our new attack with earlier subexponential quantum attacks on isogeny-based protocols, and it may be of further interest for cryptanalysis.
Nonclassical Attack on a Quantum Key Distribution System

The article is focused on research of an attack on the quantum key distribution system and proposes a countermeasure method. Particularly noteworthy is that this is not a classic attack on a quantum protocol. We describe an attack on the process of calibration. Results of the research show that quantum key distribution systems have vulnerabilities not only in the protocols, but also in other vital system components. The described type of attack does not affect the cryptographic strength of the received keys and does not point to the vulnerability of the quantum key distribution protocol. We also propose a method for autocompensating optical communication system development, which protects synchronization from unauthorized access. The proposed method is based on the use of sync pulses attenuated to a photon level in the process of detecting a time interval with a signal. The paper presents the results of experimental studies that show the discrepancies between the theoretical and real parameters of the system. The obtained data allow the length of the quantum channel to be calculated with high accuracy.
Improving Performance During Camera Surveillance by Integration of Edge Detection in IoT System

This paper is proposing an IoT-based camera surveillance system. The objective of research is to detect suspicious activities by camera automatically and take decision by comparing current frame to previous frame. Major motivation behind research work is to enhance the performance of IoT-based system by integration of edge detection mechanism. Research is making use of numerous cameras, canny edge detection-based compression module, picture database, picture comparator. Canny edge detection has been used to minimize size of graphical content to enhancing the performance system. Simulation of output of this work is made in MATLAB simulation tool. Moreover, MATLAB has been used to give comparative analysis among IoT-based camera surveillance system and traditional system. Such system requires less space, and it takes less time to inform regarding any suspicious activities.
Blockchain-Based Efficient Device Authentication Protocol for Medical Cyber-Physical Systems

As the background of application in the field of smart health care, the flexible interaction between patients and medical system is provided by medical cyber-physical systems (MCPSs) to realize all-round three-dimensional medical service. According to the controllable and credible requirements of MCPS, it needs a secure and reliable device identity authentication mechanism to build the security barrier. Based on the blockchain technology, a lightweight authentication scheme is designed for sensor/execution devices, users, and gateway nodes in MCPS. The security analysis and experimental results show that the scheme can resist the existing attacks with better efficiency; thus, our proposed scheme can be efficiently applied to the medical field.
Research on O-ring Dimension Measurement Algorithm Based on Cubic Spline Interpolation

Current O-ring dimension measurement algorithms based on machine vision are mainly whole-pixel level algorithms, which have the disadvantage of a low measurement accuracy. In order to improve the stability and accuracy of O-ring dimension measurement, a sub-pixel edge detection algorithm based on cubic spline interpolation is proposed for O-ring dimension measurement. After image pre-processing of the O-ring graphics, the whole-pixel-level O-ring edges are obtained by using a noise-resistant mathematical morphology method, and then the sub-pixel edge contours are obtained using a sub-pixel edge detection algorithm based on cubic spline interpolation. Finally, the edge curve is fitted with the least squares method to obtain its inner and outer diameter as well as the size of the wire diameter. The experimental data show that the algorithm has a mean square error of 4.8 μm for the outer diameter and 0.18 μm for the wire diameter. The outer diameter error is kept within ±100 μm and the wire diameter error can be kept within ±15 μm. Compared with the whole pixel algorithm, the measurement accuracy has been greatly improved.