Reviewerly/exHarmony huggingface dataset:
Preserving Privacy Techniques for Autonomous Vehicles

In this automated world, there will be increase in population, more city development and less space for parking results in parking related problems.In our day-to-day life vehicle became an important resource.To go anyplace we require a vehicle, when we are using vehicle, parking will be the big problem.Due to insufficient parking lots and no knowledge about the parking lots (i.e., do not the parking lots places).To park a vehicle manually we have to wait in line to take the token for parking which is a time-consuming process.There is a need to find a secured, smart, efficient system for finding nearest unoccupied parking lots, to reserve parking lot, guidance to navigate to parking lot, to negotiate parking fee and finally to secure the vehicle in parking places.Smart parking system is introduced by Intelligent Transport Systems (ITS).This article gives an overview on different smart parking services like reserving parking lots, navigating to parking lots, finding nearest parking lots, how to provide security to vehicle.It shows the cons and pros of every smart parking system.
Sentiment Analysis of Amazon Product Reviews Using Hybrid Rule-based Approach

In recent years, the retail market industry has taken a broad form to sell the products online and also to give the opportunity to customers to provide their valuable feedbacks, suggestions and recommendations.The opinion summarization and classification systems extract and identify a range of opinions about different online available products in a large text-based review set.This paper addresses and reviews the concepts of automatic identification of the sentiments expressed in the English text for Amazon and Flipkart products using Random Forest and K-Nearest Neighbor techniques.It presents a detailed comparative study of such existing sentiment analysis algorithms and methodologies on the basis of five key parameters.It results in evaluating their performance in terms of parameter usage and contributions.The paper also discusses their experimental results and challenges found.Therefore, this study shows the maximum usage of feature extraction, positive-negative sentiment, Amazon web source, mobile phone for a large set of reviews in the existing algorithms.
Recognition of Handwritten Characters using Deep Convolutional Neural Network

Handwritten character recognition (HCR) mainly entails optical character recognition. However, HCR involves in formatting and segmentation of the input. HCR is still an active area of research due to the fact that numerous verification in writing style, shape, size to individuals. The main difficult part of Indian handwritten recognition has overlapping between characters. These overlapping shaped characters are difficult to recognize that may lead to low recognition rate. These factors also increase the complexity of handwritten character recognition. This paper proposes a new approach to identify handwritten characters for Telugu language using Deep Learning (DL). The proposed work can be enhance the recognition rate of individual characters. The proposed approach recognizes with overall accuracy is 94%.
Verifying constant-time implementations by abstract interpretation

Constant-time programming is an established discipline to secure programs against timing attackers. Several real-world secure C libraries such as NaCl, mbedTLS, or Open Quantum Safe, follow this discipline. We propose an advanced static analysis, based on state-of-the-art techniques from abstract i nterpretation, to report time leakage during programming. To that purpose, we analyze source C programs and use full context-sensitive and arithmetic-aware alias analyses to track the tainted flows. We give semantic evidence of the correctness of our approach on a core language. We also present a prototype implementation for C programs that is based on the CompCert compiler toolchain and its companion Verasco static analyzer. We present verification results on various real-world constant-time programs and report on a successful verification of a challenging SHA-256 implementation that was out of scope of previous tool-assisted approaches.
ChatBot for College Website

In any service-providing organization, it is necessary to answer current or future customer’s questions for their information and for maintaining good relationship with the customers. These organizations usually have call centers with hundreds and thousands of employees to give replies to customers through chat (i.e. text) or phone calls. These kind of traditional systems need large number of employees which eventually increases the overall expenditure and manpower requirement of that organization. This paper is aimed at minimizing the manpower requirement i.e. decreasing the number of employees by implementing a chat service. This concept of chatbot has been proposed for implementation in College Website that will automatically reply to the queries of stakeholders like students, parents, etc. without any human interaction. For implementation of chatbot sequence to sequence model is used which is a deep neural network.
Secure Sublinear Time Differentially Private Median Computation

In distributed private learning, e.g., data analysis, machine learning, and enterprise benchmarking, it is commonplace for two parties with confidential data sets to compute statistics over their combined data.The median is an important robust statistical method used in enterprise benchmarking, e.g., companies compare typical employee salaries, insurance companies use median life expectancy to adjust insurance premiums, banks compare credit scores of their customers, and financial regulators estimate risks based on loan exposures.
Review of remote data integrity auditing schemes in cloud computing: taxonomy, analysis, and open issues

Cloud storage provides reliable and resilient storage infrastructure for users to store data remotely based on pay-as-you-go pricing model. Presently, many data owners in academic and business environment are choosing cloud for storing their data in the cloud to save costs. Cloud storage provides many benefits to data owners such as low capital costs, scalability, and access of data from anywhere, anytime, irrespective of location and device. Despite these appealing benefits, storage service brings security challenges such as confidentiality, integrity and availability as outsourced data is not always trustworthy due to loss of physical control and possession over data. One of the primary concerns is the integrity of data stored in the cloud. To address the remote data integrity, many researchers have focused on remote data integrity auditing (RDIA) techniques. In this paper, we give an extensive review of remote data integrity auditing techniques in the cloud computing. In our review, we present a thematic taxonomy of remote data integrity auditing techniques, investigate similarities and differences, and finally discuss critical issues to be addressed for efficient and secure designing of remote auditing protocols for cloud data storage in future research.
Implementation of Artificial Intelligence System and Traditional System: A Comparative Study

The main purpose of this paper is to study about efficient AI project implementation as a case study.For the purpose of successful AI implementation, the project plan should be complete and robust with long term view.The implementation plan includes top management decision, organization and human resource, infra structure for AI system, end user support, and company strategy.The biggest benefits of AI systems are cost reduction, quality improvement, and faster response time.The goal of this study is to provide AI system team members with successful AI project implementation guidelines compared with traditional ones as recommendation.
Post-pandemic Pedagogy: Distance Education in Greece During COVID-19 Pandemic Through the Eyes of the Teachers

Distance education had already appeared since 1970. During the first implementation period it mainly concerned adults and education at university level. In the recent years, through the internet development, distance education has acquired a new hypostasis, as innovative applications have facilitated its implementation in multiple contexts. Despite this, education remained mainly on a physical level (face to face interaction), while distance education was rather complementary (hybrid or blended learning). However, during the COVID-19 pandemic, the first pandemic in the digital era, an urgent need that led to a universal and “violent” transition to distance education arose, often without assuring the necessary preconditions. This research aims to capture the experience of teachers in Greece during this transition.
Skeleton Based View Invariant Human Action Recognition using Convolutional Neural Networks

The skeletal based human action recognition has its significant applications in the field of human computer interaction and human recognition from surveillance videos. However, the tasks suffers from the major challenges like view variance and noise in the data. These problems are limiting the performance of human action recognition. This paper focuses to solve these problems by adopting sequence based view invariant transform to effectively represent the spatio-temporal information of the skeletal data. The task of human action recognition in this paper is performed in three stages. Firstly, the raw 3D skeletal joint data obtained from the Microsoft Kinect sensor is transformed to eliminate the problem of view variations on a spatio-temporal data by implementing sequence based view invariant transform. In the second stage, the transformed joint locations of the skeletal data will be converted to RGB images by a color coding technique and forms a transformed joint location maps (TJLMs) . As a third stage, the discriminating features were extracted by the novel CNN architecture to performs the human action recognition task by means of class scores. Noticeable amount of recognition scores are achieved. Extensive experiments in four difficult 3D action datasets constantly show our method's superiority. The performance of the proposed method is compared with the other state-of-the-art methods.
Gold speciation in hydrothermal fluids revealed by in situ high energy resolution X-ray absorption spectroscopy

Abstract Gold mobilization, transfer, and concentration in the Earth’s crust are controlled by hydrothermal sulfur- and chloride-bearing fluids. Yet the exact chemical identity, structure, and stability of Au-bearing species and, in particular, the respective contributions of the sulfide (HS−) and trisulfur ion (S3⋅−) ligands to Au transport lack direct in situ evidence. Here we employed high energy resolution fluorescence detection X-ray absorption spectroscopy (HERFD-XAS) on aqueous sulfate/sulfide/S3⋅−-bearing solutions at typical hydrothermal temperatures and pressures (T = 350 °C, P = 600 bar) to reveal differences in dissolved Au spectral signatures indicative of contrasting fluid-phase Au speciation as a function of acidity and redox conditions. Combined with in situ Au solubility measurements and quantum-chemical and thermodynamic modeling, our spectroscopic data provide direct evidence for the Au(HS)S3− and Au(HS)2− complexes predominant at acidic-to-neutral and alkaline conditions, respectively. Our findings thus directly confirm a recent speciation scheme for Au in aqueous S-bearing fluids established using less direct methods, and highlight an important role of the trisulfur ion in gold mobilization and concentration in hydrothermal-magmatic deposits associated with subduction zones. More generally, our results show that HERFD-XAS enables the identification of structural and coordination features in metal complexes virtually unresolvable using classical XAS techniques. By avoiding limitations of less direct techniques, our integrated high-resolution spectroscopic approach opens perspectives for studies of the speciation and solubility of gold and other metals in high T-P fluids, and potentially silicate melts, inaccessible to direct observation in nature.
Gaussian bare-bones firefly algorithm

Firefly algorithm (FA), as a relatively recent emerged swarm intelligence algorithm, is powerful and popular for the complex real parameter global optimisation. However, the premature convergence has greatly affected the performance of original FA. To overcome this problem, we proposed a Gaussian bare-bones FA, named GBFA, in which each firefly moves to a Gaussian bare-bones method generated learning object rather than its better neighbours. The experiments are conducted on a set of widely used benchmark functions. Experimental results and comparison with the state-of-the-art FA variants have proved that the proposed algorithm is promising.
Spectral analysis of ZUC-256

In this paper we develop a number of generic techniques and algorithms in spectral analysis of large linear approximations for use in cryptanalysis. We apply the developed tools for cryptanalysis of ZUC-256 and give a distinguishing attack with complexity around 2236. Although the attack is only 220 times faster than exhaustive key search, the result indicates that ZUC-256 does not provide a source with full 256-bit entropy in the generated keystream, which would be expected from a 256-bit key. To the best of our knowledge, this is the first known academic attack on full ZUC-256 with a computational complexity that is below exhaustive key search.
Research on Copyright Protection Method of Material Genome Engineering Data Based on Zero-Watermarking

In order to effectively solve the problem of copyright protection of materials genome engineering data, this paper proposes a method for copyright protection of materials genome engineering data based on zero-watermarking technology. First, the important attribute values are selected from the materials genome engineering database; then, use the method of remainder to group the selected attribute values and extract eigenvalues; then, the eigenvalues sequence is obtained by the majority election method; finally, XOR the sequence with the actual copyright information to obtain the watermarking information and store it in the third-party authentication center. When a copyright dispute requires copyright authentication for the database to be detected. First, the zerowatermarking construction algorithm is used to obtain an eigenvalues sequence; then, this sequence is XORed with the watermarking information stored in the third-party authentication center to obtain copyright information to-be-detected. Finally, the ownership is determined by calculating the similarity between copyright information to-be-detected and copyright information that has practical significance. The experimental result shows that the zero-watermarking method proposed in this paper can effectively resist various common attacks, and can well achieve the copyright protection of material genome engineering database.
Electromagnetic wave-based extreme deep learning with nonlinear time-Floquet entanglement

Wave-based analog signal processing holds the promise of extremely fast, on-the-fly, power-efficient data processing, occurring as a wave propagates through an artificially engineered medium. Yet, due to the fundamentally weak non-linearities of traditional electromagnetic materials, such analog processors have been so far largely confined to simple linear projections such as image edge detection or matrix multiplications. Complex neuromorphic computing tasks, which inherently require strong non-linearities, have so far remained out-of-reach of wave-based solutions, with a few attempts that implemented non-linearities on the digital front, or used weak and inflexible non-linear sensors, restraining the learning performance. Here, we tackle this issue by demonstrating the relevance of time-Floquet physics to induce a strong non-linear entanglement between signal inputs at different frequencies, enabling a power-efficient and versatile wave platform for analog extreme deep learning involving a single, uniformly modulated dielectric layer and a scattering medium. We prove the efficiency of the method for extreme learning machines and reservoir computing to solve a range of challenging learning tasks, from forecasting chaotic time series to the simultaneous classification of distinct datasets. Our results open the way for optical wave-based machine learning with high energy efficiency, speed and scalability.
Research on Door Opening Operation of Mobile Robotic Arm Based on Reinforcement Learning

The traditional robotic arm control method has strong dependence on the application scenario. To improve the reliability of the mobile robotic arm control when the scene is disturbed, this paper proposes a control method based on an improved proximal policy optimization algorithm. This study researches mobile robotic arms for opening doors. At first, the door handle position is obtained through an image-recognition method based on YOLOv5. Second, the simulation platform CoppeliaSim is used to realize the interaction between the robotic arm and the environment. Third, a control strategy based on a reward function is designed to train the robotic arm and applied to the opening-door task in the real environment. The experimental results show that the proposed method can accelerate the convergence of the training process. Besides, our method can effectively reduce the jitter of the robotic arm and improve the stability of control.
Stochastic Modelling of Selfish Mining in Proof-of-Work Protocols

In blockchain-based systems whose consensus mechanisms resort to Proof-of-Work (PoW), it is expected that a miner’s share of total block revenue is proportional to their share of hashing power with respect to the rest of the network. The protocol relies on the immediate broadcast of blocks by miners, to earn precedence in peers’ local blockchains. However, a deviation from this strategy named selfish mining (SM), may lead miners to earn more than their “fair share”. In this paper, we introduce an agent-based model to simulate the dynamics of SM behaviour by a single miner as well as mining pools to understand the influence of (a) mining power distribution, (b) overlay network topology, (c) positioning of the selfish nodes within the peer to peer network. Our minimalistic model allows us to find that in high levels of latency, SM is always a more profitable strategy; our results are very robust to different network topologies and mining nodes’ centrality in the network. Moreover, the power-law distribution of the miners’ hashing power can make it harder for a selfish miner to be profitable. In addition, we analyze the effect of SM on system global efficiency and fairness. Our analysis confirms that SM is always more profitable for hashing powers representing more than one-third of the total computing power. Further, it also confirms that SM behaviour could cause a statistically significant high probability of continuously mined blocks opening the door for empirical verification of the phenomenon.
Noisy intermediate-scale quantum algorithm for semidefinite programming

Semidefinite programs (SDPs) are convex optimization programs with vast applications in control theory, quantum information, combinatorial optimization, and operational research. Noisy intermediate-scale quantum (NISQ) algorithms aim to make an efficient use of the current generation of quantum hardware. However, optimizing variational quantum algorithms is a challenge as it is an nondeterministic polynomial time-hard problem that in general requires an exponential time to solve and can contain many far from optimal local minima. Here, we present a current term NISQ algorithm for solving SDPs. The classical optimization program of our NISQ solver is another SDP over a lower dimensional ansatz space. We harness the SDP-based formulation of the Hamiltonian ground-state problem to design a NISQ eigensolver. Unlike variational quantum eigensolvers, the classical optimization program of our eigensolver is convex and can be solved in polynomial time with the number of ansatz parameters, and every local minimum is a global minimum. We find numeric evidence that NISQ SDP can improve the estimation of ground-state energies in a scalable manner. Further, we efficiently solve constrained problems to calculate the excited states of Hamiltonians, find the lowest energy of symmetry constrained Hamiltonians, and determine the optimal measurements for quantum state discrimination. We demonstrate the potential of our approach by finding the largest eigenvalue of up to ${2}^{1000}$ dimensional matrices and solving graph problems related to quantum contextuality. We also discuss NISQ algorithms for rank-constrained SDPs. Our work extends the application of NISQ computers onto one of the most successful algorithmic frameworks of the past few decades.
KLLR: A Scale-dependent, Multivariate Model Class for Regression Analysis

Abstract The underlying physics of astronomical systems govern the relation between their measurable properties. Consequently, quantifying the statistical relationships between system-level observable properties of a population offers insights into the astrophysical drivers of that class of systems. While purely linear models capture behavior over a limited range of system scale, the fact that astrophysics is ultimately scale dependent implies the need for a more flexible approach to describing population statistics over a wide dynamic range. For such applications, we introduce and implement a class of kernel localized linear regression (KLLR) models. KLLR is a natural extension to the commonly used linear models that allows the parameters of the linear model—normalization, slope, and covariance matrix—to be scale dependent. KLLR performs inference in two steps: (1) it estimates the mean relation between a set of independent variables and a dependent variable and; (2) it estimates the conditional covariance of the dependent variables given a set of independent variables. We demonstrate the model's performance in a simulated setting and showcase an application of the proposed model in analyzing the baryonic content of dark matter halos. As a part of this work, we publicly release a Python implementation of the KLLR method.