Reviewerly/exHarmony huggingface dataset:
Mobile Terminal Identity Authentication Method Based on IBC

With the rapid development of the power mobile Internet, traditional identity authentication and authentication modes still have identity information theft, are unable to prevent illegal behaviors of internal users, etc., which can no longer meet the security requirements of identity authentication in mobile Internet services. In response to this problem, this paper proposes a mobile terminal identity authentication method on the Identity-Based Cryptography (IBC). During registration, the user’s voice is collected through the mobile terminal to establish an identity vector (i-vector) voiceprint model, and features are extracted and added to the identity mark to generate a user ID and a corresponding identity password system; during authentication, the legitimacy of the user is judged based on voice recognition to prevent illegal user intrusion, And then based on the identity password combined with the symmetric encryption algorithm AES to encrypt and decrypt data to achieve terminal identity authentication to resist common attacks in the mobile Internet. Finally, the experimental analysis shows that the method effectively improves the security of the power mobile Internet identity authentication process and has the advantages of low cost and high efficiency. In the power mobile Internet business scenario, this method greatly improves the identification ability of illegal users and the resistance to network malicious attacks.
Detecting Anomalies in Natural Gas Production: A Boosting Tree Based Model

Natural gas is one of the important energy sources. However, during the production process of natural gas, abnormal events often occurred due to various factors so that the pumping equipment could not work. At present, the detection of abnormal production of gas wells mainly relies on the personal experience of engineers. The continuous production of gas well data puts huge pressure on limited manpower. Moreover, the results of manual judgment are often unreliable due to personal subjectivity, and problems such as failure to find abnormalities in time. The objective of this paper is to establish a fast and reliable data-driven anomaly detection framework. Its focus is managing and processing a high volume of data to improve operational efficiency, enhance decision making and mitigate risks in the workplace. The proposed framework employs a state-of-the-art algorithm, called boosting tree, which can not only identify point anomalies but also find context anomalies based on historical data. Comparing the test results with the manual annotation results on several real gas production datasets, the results show that the proposed framework is proficient at detecting anomalies.
Vehicle Perception from a Single Image for Autonomous Driving Using Deformable Model Representation and Deep Learning

We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image for autonomous driving. Our approach combines the strengths of deep learning and the elegance of traditional techniques from part-based deformable model representation to produce high-quality 3D models in the presence of severe occlusions. We present a new part-based deformable vehicle model that is used for instance segmentation and automatically generate a dataset that contains dense correspondences between 2D images and 3D models. We also present a novel end-to-end deep neural network to predict dense 2D/3D mapping and highlight its benefits. Based on the dense mapping, we are able to compute precise 6-DoF poses and 3D reconstruction results at almost interactive rates on a commodity GPU. We have integrated these algorithms with an autonomous driving system. In practice, our method outperforms the state-of-the-art methods for all major vehicle parsing tasks: 2D instance segmentation by 4.4 points (mAP), 6-DoF pose estimation by 9.11 points, and 3D detection by 1.37. Moreover, we will release all of the source code, dataset, and the trained model on Github.
Inference for Optimal Differential Privacy Procedures for Frequency Tables

When releasing data to the public, a vital concern is the risk of exposing personal information of the individuals who have contributed to the data set. Many mechanisms have been proposed to protect individual privacy, though less attention has been dedicated to practically conducting valid inferences on the altered privacy-protected data sets. For frequency tables, the privacy-protection-oriented perturbations often lead to negative cell counts. Releasing such tables can undermine users’ confidence in the usefulness of such data sets. This paper focuses on releasing one-way frequency tables. We recommend an optimal mechanism that satisfies ϵ-differential privacy (DP) without suffering from having negative cell counts. The procedure is optimal in the sense that the expected utility is maximized under a given privacy constraint. Valid inference procedures for testing goodness-of-fit are also developed for the DP privacy-protected data. In particular, we propose a de-biased test statistic for the optimal procedure and derive its asymptotic distribution. In addition, we also introduce testing procedures for the commonly used Laplace and Gaussian mechanisms, which provide a good finite sample approximation for the null distributions. Moreover, the decaying rate requirements for the privacy regime are provided for the inference procedures to be valid. We further consider common users’ practices such as merging related or neighboring cells or integrating statistical information obtained across different data sources and derive valid testing procedures when these operations occur. Simulation studies show that our inference results hold well even when the sample size is relatively small. Comparisons with the current field standards, including the Laplace, the Gaussian (both with/without post-processing of replacing negative cell counts with zeros), and the Binomial-Beta McClure-Reiter mechanisms, are carried out. In the end, we apply our method to the National Center for Early Development and Learning’s (NCEDL) multi-state studies data to demonstrate its practical applicability.
A New Identity-Based Multivariate Signature Scheme

Authentication of public keys in asymmetric cryptography is an important objective to fulfil. Identity-based schemes are an efficient solution for this scenario. There are various identity-based schemes that are based on either factoring problem or DLP problem, but these problems can be solved in polynomial time using quantum computers. Multivariate cryptography is one of the main alternatives for the construction of post-quantum digital signature schemes. Digital signature construction needs modest computations in multivariate public key cryptography, so these schemes are considered quite efficient. In this paper, we present a new identity-based signature scheme in which the signature size and user key size are relatively small. The design of our proposed signature scheme is based on MQDSS construction.
GAN Fingerprints in Face Image Synthesis

Abstract The availability of large-scale facial databases, together with the remarkable progresses of deep learning technologies, in particular Generative Adversarial Networks (GANs), have led to the generation of extremely realistic fake facial content, raising obvious concerns about the potential for misuse. Such concerns have fostered the research on manipulation detection methods that, contrary to humans, have already achieved astonishing results in various scenarios. This chapter is focused on the analysis of GAN fingerprints in face image synthesis.
An Express Transportation Monitoring System Based on S-CNN

Rough Handling of Express Parcels (RHEP) means that express parcels are not handled as required, which is one of the increasingly prominent problems in the express industry. It not only brings bad effects to the industry but also causes serious social problems such as resource waste and environmental pollution. Based on S-CNN, for three typical types of RHEP (dropping, throwing, and kicking), an express transportation monitoring system is proposed in this paper. The system is mainly composed of terminals, servers, and client. The system can collect acceleration data of parcels, process data, extract features and recognize RHEP of express transportation. Simulation experiment of RHEP in the laboratory, the recognition accuracy of S-CNN is up to 95.3%. Compared with SVM, BP, and S-CNN, more than 99% of samples of express transportation have a predicted probability of RHEP more than 0.95, which verifies the effectiveness of the intelligent recognition method in express transportation.
Night Vision, Day &amp; Night Prediction with Object Recognition (NVDANOR) Model

Night vision has been one of the key developments in Computer Vision system as it gave us a key point to modify an area where humans have the least ability to perform. Object detection is reliable and efficient tool to recognize objects in scenarios such as daytime images where the illumination is great. However, night pictures tend to be challenging to recognize for human being and it usually brings us less data than the images that are taken during day due to poor contrast against its background that interfere with clearly recognizing and labeling them. Different models have been proposed for night vision image processing which use denoising, deblurring and enhancing technique however, other methods can be used in order to enhance that picture and make them as usable and understandable as possible. In addition, different prediction methods and models have been developed in order to achieve different degrees of object recognition in that image, still those results and accuracy can be improved for better results. In this paper, we propose a model that can predict which time of the day it is in the picture with help of calculating average brightness on the images of different time periods with HSV. The model includes ResNet-50 and VGG-16 classifiers that can also recognize the objects and buildings in the image with good accuracy. Implementation of deep learning algorithms and image brightness enhancement tools helped us to achieve improved accuracy and better prediction. The model achieved 94% prediction results when it comes to day and night prediction and 93.75% in object detection on night images.
Spark Distributed Real-Time Data and GPU Parallel Computing Based on 5G Virtual Reality

5G virtual reality has attracted many manufacturers and users with its unique immersion, interactivity and imagination characteristics, which has become the focus of new markets. The purpose of this article is to use Spark distributed real-time data system and GPU parallel computing to quickly process and analyze data. This article mainly designs a general-purpose real-time data analysis and processing system based on Spark, which mainly includes new ETL and real-time processing engine modules, and is committed to achieving higher real-time performance than traditional Hadoop. And realize fast calculation. At the same time there is universality and stability. Includes real-time flow calculations. Fast batch processing and machine learning The various types of data computers are included in this article by preparing the cutting device and adjusting the cutting output. The device is ready to effectively terminate the CUDA environment. The cudamalloc function is used to allocate a linear space of bytes to the device, and then transfer the data from the host to the device to determine the number of GPU blocks and threads. GPU parallel computing can increase the data processing speed by 27%, while the secondary programming algorithm can reduce the optimization time of the cup by 12%.
Safer together

A pioneer of digital contact tracing discusses the considerations that went into building the world's first national contact tracing app and ponders the future of the technology.
The b-symbol weight hierarchy of the Kasami codes

The symbol-pair read channel was first proposed by Cassuto and Blaum. Later, Yaakobi et al. generalized it to the $b$-symbol read channel. It is motivated by the limitations of the reading process in high density data storage systems. One main task in $b$-symbol coding theory is to determine the $b$-symbol weight hierarchy of codes. In this paper, we study the $b$-symbol weight hierarchy of the Kasami codes, which are well known for their applications to construct sequences with optimal correlation magnitudes. The complete symbol-pair weight distribution of the Kasami codes is determined.
Experimental evaluation of the effect of phoneme time stretching on speaker embedding

For an indefinite length spectrogram sequence of phonemes, we experimentally verified two methods of obtaining speaker embedding by transforming it to fixed length: adding padding and time stretching. We confirmed that both methods can maintain the extraction performance. We also confirm that the fixed frame length does not affect the results.
The Economic Behavior Theory -The Nudge Theory : An Appraisal

The behavioral economics is making a significant impact on economic theory and economic policy. Behavioral economics with intuitive appeal, seeks to work with human limitations. Nudge theory becomes an important aspect to study about behavioral economics, political theory and behavioral sciences, as ways to influence the behavior and decision-making of groups or individuals. Nudge theory explains consumer behavior can be influenced by small suggestions and positive views. Nudges decrease low market performance, save the Government money, encourage the actions and help to increase the efficiency of resource use and their effect. Nudge helps to choose an option socially desirable beneficial for better public healthcare, civic sense and social harmony. Humans being need encouragement or intervention as nudge to do what is best for the country or society at large .
Modal Logics and Local Quantifiers: A Zoo in the Elementary Hierarchy

Abstract We study a family of modal logics interpreted on tree-like structures, and featuring local quantifiers $$\exists ^{k}p$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:msup> <mml:mo>∃</mml:mo> <mml:mi>k</mml:mi> </mml:msup> <mml:mi>p</mml:mi> </mml:mrow> </mml:math> that bind the proposition p to worlds that are accessible from the current one in at most k steps. We consider a first-order and a second-order semantics for the quantifiers, which enables us to relate several well-known formalisms, such as hybrid logics , $$\textsf {S5Q}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>S</mml:mi> <mml:mn>5</mml:mn> <mml:mi>Q</mml:mi> </mml:mrow> </mml:math> and graded modal logic . To better stress these connections, we explore fragments of our logics, called herein round-bounded fragments. Depending on whether first or second-order semantics is considered, these fragments populate the hierarchy $${2\textsc {NExp} \subset 3\textsc {NExp} \subset \cdots }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>NE</mml:mi> <mml:mstyle> <mml:mi>X</mml:mi> <mml:mi>P</mml:mi> </mml:mstyle> <mml:mo>⊂</mml:mo> <mml:mn>3</mml:mn> <mml:mi>NE</mml:mi> <mml:mstyle> <mml:mi>X</mml:mi> <mml:mi>P</mml:mi> </mml:mstyle> <mml:mo>⊂</mml:mo> <mml:mo>⋯</mml:mo> </mml:mrow> </mml:math> or the hierarchy $${2\textsc {AExp}_{pol} \subset 3\textsc {AExp}_{pol} \subset \cdots }$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>2</mml:mn> <mml:mi>AE</mml:mi> <mml:msub> <mml:mstyle> <mml:mi>X</mml:mi> <mml:mi>P</mml:mi> </mml:mstyle> <mml:mrow> <mml:mi>pol</mml:mi> </mml:mrow> </mml:msub> <mml:mo>⊂</mml:mo> <mml:mn>3</mml:mn> <mml:mi>AE</mml:mi> <mml:msub> <mml:mstyle> <mml:mi>X</mml:mi> <mml:mi>P</mml:mi> </mml:mstyle> <mml:mrow> <mml:mi>pol</mml:mi> </mml:mrow> </mml:msub> <mml:mo>⊂</mml:mo> <mml:mo>⋯</mml:mo> </mml:mrow> </mml:math> , respectively. For formulae up-to modal depth k , the complexity improves by one exponential.
Financial Innovation in Sierra Leone: Is the Money Demand Still Stable?

This study seeks to examine the stability of the money demand function in Sierra Leone taking into account financial innovation and structural break for the period 1980 to 2016 using the autoregressive distributed lag (ARDL) framework.The empirical results show that there is a unique cointegrated and stable long-run relationship among real broad money and its determinants.The estimated results also revealed that in the long-run, financial innovation, real income, inflation rate and exchange rate significantly impact real money balances in Sierra Leone.Specifically, financial innovation has a negative relationship with real money balances.This implies that financial innovation is crucial in explaining money demand in Sierra Leone, given that financial innovations are becoming more prominent in aiding financial intermediation.In the short-run, only financial innovation and inflation have an impact on real money balance.All the other variables in the model are not statistically significant.Finally, the CUSUM and CUSUMSQ tests revealed that demand for real money balances in Sierra Leone is stable, despite the inclusion of financial innovation and accounting for structural break.
Identifying Forged Digital Image Using Adaptive Over Segmentation and Feature Point

Wide availability of free image editing tools has made it very convenient to edit digital images. Image editing with wrong intensions is becoming a serious problem. The existing detection method deals with feature point equating and adaptive over segmentation in this work. Suggested conspire coordinates reality-based forgery and block-based detection techniques. Image tempering with bad intentions is becoming more common in the news in media field, patient data in medical field, and several other fields. This paper proposes an adaptive over segmentation-based detection of forged images. To begin, suggested algorithm slices the input picture into well separated and asymmetrical blocks suitably. At that point, the points extricated from scale invariant feature transform (SIFT) are again extricated from individual block. The features such obtained are called block features (BFs). To find labeled feature points, these BFs are equated with one another; this technique roughly shows the speculated fraud locales called surmised forgery locales. To identify the forgery locales correctly, the paper suggest an algorithm called forgery local extrication. Using the algorithm, the super pixels are replaced by the feature points and afterward combines similar local color features into the feature blocks to produce the unified locales. Lastly, to produce the detected forgery locales, we apply the morphological operation to the unified locales. The analyzed outcome and comparative analysis demonstrates that the proposed detection plan accomplishes prominent detection outcomes.
Consistent Training and Decoding For End-to-end Speech Recognition Using  Lattice-free MMI

Recently, End-to-End (E2E) frameworks have achieved remarkable results on various Automatic Speech Recognition (ASR) tasks. However, Lattice-Free Maximum Mutual Information (LF-MMI), as one of the discriminative training criteria that show superior performance in hybrid ASR systems, is rarely adopted in E2E ASR frameworks. In this work, we propose a novel approach to integrate LF-MMI criterion into E2E ASR frameworks in both training and decoding stages. The proposed approach shows its effectiveness on two of the most widely used E2E frameworks including Attention-Based Encoder-Decoders (AEDs) and Neural Transducers (NTs). Experiments suggest that the introduction of the LF-MMI criterion consistently leads to significant performance improvements on various datasets and different E2E ASR frameworks. The best of our models achieves competitive CER of 4.1\% / 4.4\% on Aishell-1 dev/test set; we also achieve significant error reduction on Aishell-2 and Librispeech datasets over strong baselines.
Attribute Inference Attack of Speech Emotion Recognition in Federated Learning Settings

Speech emotion recognition (SER) processes speech signals to detect and characterize expressed perceived emotions. Many SER application systems often acquire and transmit speech data collected at the client-side to remote cloud platforms for inference and decision making. However, speech data carry rich information not only about emotions conveyed in vocal expressions, but also other sensitive demographic traits such as gender, age and language background. Consequently, it is desirable for SER systems to have the ability to classify emotion constructs while preventing unintended/improper inferences of sensitive and demographic information. Federated learning (FL) is a distributed machine learning paradigm that coordinates clients to train a model collaboratively without sharing their local data. This training approach appears secure and can improve privacy for SER. However, recent works have demonstrated that FL approaches are still vulnerable to various privacy attacks like reconstruction attacks and membership inference attacks. Although most of these have focused on computer vision applications, such information leakages exist in the SER systems trained using the FL technique. To assess the information leakage of SER systems trained using FL, we propose an attribute inference attack framework that infers sensitive attribute information of the clients from shared gradients or model parameters, corresponding to the FedSGD and the FedAvg training algorithms, respectively. As a use case, we empirically evaluate our approach for predicting the client's gender information using three SER benchmark datasets: IEMOCAP, CREMA-D, and MSP-Improv. We show that the attribute inference attack is achievable for SER systems trained using FL. We further identify that most information leakage possibly comes from the first layer in the SER model.
Blockchain as an IoT Intermediary

Blockchain technology provides a private, secure, transparent decentralized exchange of data. Also, blockchain is not limited to a particular area, but it has a wide range of applications and can be integrated into a variety of Internet interactive systems. For example, the Internet of Things (IoT), supply chain tracking, Electronic Health Records (EHR), digital forensics, identity management, trustless payments, and other key business elements will all benefit from its implementation. Next layer solutions such as Ethereum 2.0, Polkadot, Cardano, and other Web 3.0 technologies provide developers versatility. Moreover, these platforms utilize smart contracts which are similar to standard, traditionalized software during development but offer key utilities to end-users such as online wallets, secure data with transparent rules. Blockchain is receiving a lot of attention in educational technology (EduTech) as it aims to achieve a more transparent and multipurpose educational system. In addition to smart contract technology which defines how data should be registered, gathered and processed, blockchain can be used as an IoT intermediary for mobile usage. Therefore, we implemented an educational learning platform powered by blockchain technology to examine feasibility in industry and academic environment. In essence, this is a web application which is adapted to mobile platform and connected to blockchain for crucial data exchanges. In this paper we want to emphasize the potential of blockchain technology in multiple sectors as well as the need to really understand the underlying principles which are allowing disruptability of traditional centralized software solutions.