Reviewerly/exHarmony huggingface dataset:
Differentially private distributed logistic regression with the objective function perturbation

Distributed learning is a very effective divide-and-conquer strategy for dealing with big data. As distributed learning algorithms become more and more mature, network security issues including the risk of privacy disclosure of personal sensitive data, have attracted high attention and vigilance. Differentially private is an important method that maximizes the accuracy of a data query while minimizing the chance of identifying its records when querying from the given data. The known differentially private distributed learning algorithms are based on variable perturbation, but the variable perturbation method may be non-convergence and the experimental results usually have large deviations. Therefore, in this paper, we consider differentially private distributed learning algorithm based on objective function perturbation. We first propose a new distributed logistic regression algorithm based on objective function perturbation (DLR-OFP). We prove that the proposed DLR-OFP satisfies differentially private, and obtain its fast convergence rate by introducing a new acceleration factor for the gradient descent method. The numerical experiments based on benchmark data show that the proposed DLR-OFP algorithm has fast convergence rate and better privacy protection ability.
Intelligent defect detection for Power Patrol Inspection

Abstract In this paper, the insulator string images captured by Using unmanned aerial vehicles (UAVs) in the power inspection are taken as the research object. The image processing and deep learning methods are used to label the images, and the self-explosion fault of the identified and segmented insulator strings is identified and located. In the process of semantic segmentation of insulator string images, the given image data set is enhanced, and images are exchanged with image processing algorithms. Based on SegNet, each pixel in the picture is classified, and then the self-encoding is used for reference, and the image is first encoded and then decoded. The coding layer before the model uses the VGG-16 network to extract smaller pictures, so as to meet the requirements of semantic segmentation, and the decoding layer after the model obtains the classification probability value of each point and classifies each point. The experimental results show that the SegNet algorithm can realize the recognition and segmentation of insulator string images under complex conditions with high accuracy. In order to realize the identification and location of the self-exploding fault position of the insulator string, the training model can be used to detect the position of the self-exploding insulator in the image. First, the data of target detection is preprocessed, and then the data set transformation method of rotation transformation and image mirroring is used; then the SSD model is selected to identify and locate the self-explosion position of the insulator. The SSD model combines the advantages of the Faster R-CNN model and the YOLO model. It runs faster and has higher detection accuracy, which meets practical applications. The experimental results show that the SSD algorithm is more targeted to the insulator data set, and can improve the speed and accuracy of insulator identification and positioning.
A Class of <i>n</i>-to-1 Binomials over Finite Fields

[see formula in PDF]-to-1 mappings have many applications in combinatorial design, coding theory and cryptography. In this paper, by using piecewise method and monomials on subsets of [see formula in PDF]-th roots of unity, we show a class of [see formula in PDF]-to-1 binomials having the form [see formula in PDF] over [see formula in PDF].
Improved Lightweight YOLOv3 model for Target Detection Algorithm

When detecting small objects in interior situations, the classic object detection algorithm performs poorly in terms of real-time detection task and high precision detection task. This paper suggests an optimized tiny-YOLOv3-Shufflenetv2 light-weight model based on indoor scenes. The scheme adopts the fusion light-weight network which combines ShuffleNetv2 and YOLOv3, it reduces the complexity of the model to meet the lightweight requirements while ensuring good detection results for deployment to mobile robots. Also in this paper, an indoor small target object dataset, indoor-2022, is created to improve and optimize the model for the data images. YOLOv3, YOLOv3-Shufflenetv2, and tiny-YOLOv3-Shufflenetv2 are trained and tested on the indoor-2022 small target dataset in the Pytorch framework. The experimental findings indicate that in the indoor-2022 dataset. Compared with the single YOLOv3 model for object detection tasks, the fusion improved model used in this article improves the recognition ability of small objects in indoor images, With a 10-fold reduction in model size and a 4-fold increase in detection speed, only results in 1.6% reduction in the mean accuracy (mAP), and the comparison experiments with the current stage of traditional target detection algorithms validate the proposed tiny-YOLOv3-Shufflenetv2 model is verified to be superior and feasible. The optimized model in this article reduces mannequin parameters and model size while additionally ensuring the accuracy and velocity of inspection, and meets the requirements for deployment on indoor mobile robots.
Factorization Method of the Elliptic Curve

Abstract The elliptic curve is an important topic in number theory. In 1987, Lenstra discovered the elliptic-curve factorization method (ECM). [4] Nevertheless, until now, no research can have complete detailed codes in Wolfram Mathematica. This article will state the definition of the elliptic curve, analyze this ECM algorithm, and build a complete code s in Mathematica. Finally, completed factorization for 1820099 by experiment, which can prove that the code can complete ECM, but it may take much time to calculate.
DSMFFNet: Depthwise separable multiscale feature fusion network for bridge detection in very high resolution satellite images

Bridge detection methods based on deep learning have many parameters, complex calculations, and serious errors and missed detections for multiscale bridges. To solve the above problems, a depth-wise separable multiscale feature fusion network (DSMFFNet) is proposed for efficient and accurate bridge detection in very high resolution satellite images (VHR). First, depth-wise separable convolution was used to build a backbone feature extraction network to extract the bridge features. Second, to better match bridges of different scales, multiscale receptive fields were obtained by multibranch parallel dilated convolution at the last layer of the feature map. Then, to make full use of the details and semantic information of the bridges at different depths, the three effective feature layers of the bridges at different levels are fused by a multiscale feature pyramid. The experimental results showed that the mean average precision (mAP) and frame per second (FPS) of the proposed method reach 94.26% and 60.04%, which can lead most of the mainstream object detection networks in accuracy and speed and can be integrated into the mobile end to complete the task of high-precision and fast bridge detection.
Performance optimization of CuO-ZnO ceramic electrode on the electrocoagulation of wastewater

Copper oxide (CuO) and zinc oxide (ZnO) were used as a matrix and reinforcement in the compression molding technique to produce conductive ceramic electrodes. CuO-ZnO-based ceramic composites were fabricated through conventional pressing with CuO:ZnO ratios of 75 %:25 %, 80 %:20 %, 85 %:15 %, 90 %:10 %, and 95 %:5 %. CuO and ZnO powders were sieved, dried, mixed, and then compressed at 300 MPa for 10 min to form a pellet before being burned for 3 h at 800, 900, and 1000 °C in a furnace. The sample's density, porosity, and electrical conductivity were measured. In addition, SEM, XRD analysis, and palm oil wastewater electrocoagulation were carried out. The optimal density value of CuO-ZnO-based ceramic composites ranged from 8.564 × 103 to 9.205 × 103 kg m−3. The optimal composition of the CuO:ZnO in the synthesized composites, namely 95 %:5 %, yielded the lowest porosity values (12.1 %-35.4 %), while the 75 %:25 % composition produced the highest porosity values (20.8 %–88.3 %). The optimum porosity value (12.1 %) was obtained by CuO:ZnO composite composition of 95 %:5 % with a sintering temperature of 1000 °C. This composite also generates a conductivity value of 1.019–16.897 S/m. The interaction of Zn2+ ions from ZnO crystals with CuO crystals increases the size of CuO and ZnO crystals, as determined by XRD analysis. Based on the findings of this study, CuO-ZnO ceramic electrodes could be utilized in industrial wastewater treatment as the water quality test result met the requirements of Minister of Environment Decree No. 51/MENLH/10/1995.
An approximation algorithm for querying inconsistent knowledge bases

Several medical applications deal with inconsistent knowledge bases, namely information that possibly violates given constraints, as they may not be enforced or satisfied. For instance, inconsistency may arise in clinical data integration, where multiple autonomous sources are integrated together: even if the sources are separately consistent, the integrated database may be inconsistent. A challenging problem in inconsistent clinical knowledge base management is extracting reliable information. The goal is to return reliable answers to queries even in the presence of inconsistent background data. In this regard, the majority of the proposals are based on the consistent query answering approach, where query answers are those obtained from all repairs, that are maximal consistent subsets of the knowledge base’s facts. We present a sound and polynomial-time approximation algorithm for solving the coNP-complete problem of consistent query answering. Our approach returns more consistent answers compared to those returned by state-of-the-art approaches, as they might discard facts including useful information.
Cheap as chips: An accessible chip off acquisition method for ball grid array (BGA) integrated circuits in digital investigations

Chip off acquisition has steadily been used in digital investigations as an advanced data acquisition technique. This method has typically been reserved for devices where less invasive methods have been unsuccessful in data recovery. After a review of available literature, limited publications were found to define and discuss a detailed chip off methodology, especially using accessible and low-cost equipment. Therefore, demonstrating a lack of knowledge share and standardisation in this space. This paper creates a methodology for chip off acquisition, that examiners can follow, using an array of equipment. The methodology was developed using accessible resources in a variety of formats and experimental research. Once constructed the method was tested in a collection of scenarios and utilised in research and consultancy. This demonstrated areas where the method was positively implemented and areas where updates could improve the overall success of the methodology. Following the evaluation, a 6-stage process was formulated: deconstruction, identification, removal, restoration, determine and perform.
Approximating the first passage time density from data using generalized Laguerre polynomials

This paper analyzes a method to approximate the first passage time probability density function which turns to be particularly useful if only sample data are available. The method relies on a Laguerre-Gamma polynomial approximation and iteratively looks for the best degree of the polynomial such that the fitting function is a probability density function. The proposed iterative algorithm relies on simple and new recursion formulae involving first passage time moments. These moments can be computed recursively from cumulants, if they are known. In such a case, the approximated density can be used also for the maximum likelihood estimates of the parameters of the underlying stochastic process. If cumulants are not known, suitable unbiased estimators relying on k-statistics are employed. To check the feasibility of the method both in fitting the density and in estimating the parameters, the first passage time problem of a geometric Brownian motion is considered.
Sample Complexity of Learning Multi-value Opinions in Social Networks

We consider how many users we need to query in order to estimate the extent to which multi-value opinions (information) have propagated in a social network. For example, if the launch date of a new product has changed many times, the company might want to know to which people the most current information has reached. In the propagation model we consider, the social network is represented as a directed graph, and an agent (node) updates its state if it receives a stronger opinion (updated information) and then forwards the opinion in accordance with the direction of its edges. Previous work evaluated opinion propagation in a social network by using the probably approximately correct (PAC) learning framework and considered only binary opinions. In general, PAC learnability, i.e., the finiteness of the number of samples needed, is not guaranteed when generalizing from a binary-value model to a multi-value model. We show that the PAC learnability of multi-value opinions propagating in a social network. We first prove that the number of samples needed in a multi-opinion model is sufficient for $$(k-1)\log (k-1)$$ times the number of samples needed in a binary-opinion model, when $$k~(\ge 3)$$ is the number of opinions. We next prove that the upper and lower bounds on the number of samples needed to learn a multi-opinion model can be determined from the Natarajan dimension, which is a generalization of the Vapnik-Chervonenkis dimension.
A System for Tracking Organic Honey Production Using Blockchain Technologies

This paper aims to tackle a problem of distinguishing organic honey products from the conventional ones. The main goal is to give an example of blockchain oriented system for tracking the process of organic honey production in order to provide transparent information about the origin of the product that cannot be modified. Focus will be put on individual beekeepers and end consumers. The paper gives a brief theoretical background of blockchain technologies and Internet of Things (IoT). Designing a system for tracking honey production includes creating business model, process model, data model and model of smart contract. The paper also gives an example of prototype development including a prototype device with appropriate sensors and also a proposed application layout. System can be additionally expanded to include different stakeholders, such as distributors and retailers.
Activation‐based recurrent learning method for wearable sensor data processing in human activity recognition

Abstract Human activity recognition using wearable sensors is a universal application for providing additional real‐time support in AAL and medical healthcare systems. Input signals and sensor data are analyzed for the features, followed by correlation analysis to achieve better recognition accuracy. In this article, activation‐based recurrent learning for human activity detection is introduced. This method is based on recurrent neural networks that pre‐classifies the features and extract them for time and rate based on the input signals. Following the pre‐classification, the repetitive learning estimates the features for the correlation and activities to reduce false‐negative errors in processing. This recurrent process is validated using a conditional activation function and rectified linear unit for joint and concurrent layer processing. The combined processing feature of this proposed method helps to improve the accuracy by 92% and recall by 94.5% by reducing the computation time. The performance of the proposed method is analyzed for six activities, varying iterates, and at time intervals.
AN Using Local Search in Multi-issue Bilateral and Repeated Negotiation

Designing an automated agent for Human-Agent Negotiation is a challenging task. Especially in the domain that combines multi-issue bilateral negotiations and repeated negotiations. In this domain, the agents negotiate with humans over more than one item, and there are several rounds of negotiation in each game. Designing this kind of agent can be very challenging. Our agent needs to estimate the preferences of the human opponent in real-time, proposing fair offers that will be excepted by the human opponent but taking into account, not proposing offers that don’t increase the agent’s score. On the other hand, local search algorithms have proven to be effective in a variety of fields in artificial intelligence. In this paper, we present a novel approach for an automated agent for this type of negotiation by using local search algorithms, in particular: Simulated Annealing and Hill Climbing. As we analyze the results from our experiments and compare the local search algorithms, we show that local search algorithms can be more efficient in Human-Agent negotiation than traditional methods. Moreover, our agent is capable of negotiating efficiently and outperforming the human opponent.
A Novel Approach to the Team Estimations in the Agile Software Development

One of the main challenges that organizations are currently facing is resource allocation and planning. It is becoming very hard for the organization or team to achieve excellence in results and to deliver the requirements to the clients quickly and in a timely manner. Some of the road blockers and factors for that could be found in: poor initiation, wrong estimation, lack of clear objective, failure to communicate properly and everything done by one individual. In Agile IT operations, estimation techniques can be used to properly plan for resource allocation. This is especially important and it could be very challenging in the remote working environment that was introduced recently. The main objective of this paper is to propose a new decision support system for estimates. To achieve this, next two secondary objectives were defined: (a) review the existing estimation techniques that are widely used by companies for project management and proposed by other researchers; (b) explore different aspects of the estimation process and identify areas for the potential improvement, which is achieved by analyzing the results collected from the survey that was organized in the previous period. Findings from the secondary objectives were used to formulate improvements in the area of team consensus in estimates, logical approach when calculating the estimates and feedback, which are incorporated in the mentioned decision support system.
MTIRL: Multi-trainer Interactive Reinforcement Learning System

Interactive reinforcement learning can effectively facilitate the agent training via human feedback. However, such methods often require the human teacher to know what is the correct action that the agent should take. In other words, if the human teacher is not always reliable, then it will not be consistently able to guide the agent through its training. In this paper, we propose a more effective interactive reinforcement learning system by introducing multiple trainers, namely Multi-Trainer Interactive Reinforcement Learning (MTIRL), which could aggregate the binary feedback from multiple non-perfect trainers into a more reliable reward for an agent training in a reward-sparse environment. In particular, our trainer feedback aggregation experiments show that our aggregation method has the best accuracy when compared with the majority voting, the weighted voting, and the Bayesian method. Finally, we conduct a grid-world experiment to show that the policy trained by the MTIRL with the review model is closer to the optimal policy than that without a review model.
Design of Conversational Components to Facilitate Human-Agent Negotiation

With burgeoning interest in the industry and among citizens about the potential of human-AI partnerships [10], academic researchers have been pushing the frontier of new modalities of peer-level and ad-hoc human-agent collaboration [5, 11]. We are particularly interested in research on agents representing human users in negotiating deals with other human and autonomous agents [9]. We present the design motivation and critical components of the conversational aspect of our agents entry into the Human-Agent League of the Automated Negotiation Agent Competition. We explore how language can be used to promote human’s likeability, even in the domain of a competitive negotiation.
Efficient and secure e‐voting scheme using elliptic curve cryptography

Abstract Voting allows the people to elect their representative and express their preferences. In modern day democracy, voting is one fundamental and most important tool for election. To strengthen this process, efforts must be made to achieve a confirmable and transparent voting system. Naturally, the veracity of the election process is of ultimate importance for the honor of the democracy itself. The online voting or e‐voting system is a cost‐effective procedure, which saves a lot of money and time spent for organizing the election. However, security, integrity of data and privacy of the voter are the increasing concerns in this context. In this paper, we have done a thorough literature survey of the various latest schemes in this context and found that the schemes prone to several security and privacy threats. Moreover, a major issue with all these schemes is that they are inefficient in terms of computation and communication overheads. With this motivation, we present a novel and efficient e‐voting scheme to tackle the security and privacy concerns. The proposed scheme is designed using the concept of blind signature, anonymous channel and trust worthy entities where elliptic curve cryptography (ECC) works as a backbone. The proposed scheme is formally evaluated using well known AVISPA simulation tool, which simulates the attack model using CL‐AtSe as well as OFMC backend according to Dolev‐Yao threat model. In both the cases, all the possible security threats are being nullified by the proposed scheme and results in safe communication. Also, the scheme is analyzed against all the possible attacks that jeopardize the integrity of the existing voting systems. Moreover, the proposed scheme is also compared with other prevailing schemes in terms of computation and communication overheads and found more efficient.
Data‐driven analysis and predictive modeling on COVID‐19

The coronavirus (COVID-19) started in China in 2019, has spread rapidly in every single country and has spread in millions of cases worldwide. This paper presents a proposed approach that involves identifying the relative impact of COVID-19 on a specific gender, the mortality rate in specific age, investigating different safety measures adopted by each country and their impact on the virus growth rate. Our study proposes data-driven analysis and prediction modeling by investigating three aspects of the pandemic (gender of patients, global growth rate, and social distancing). Several machine learning and ensemble models have been used and compared to obtain the best accuracy. Experiments have been demonstrated on three large public datasets. The motivation of this study is to propose an analytical machine learning based model to explore three significant aspects of COVID-19 pandemic as gender, global growth rate, and social distancing. The proposed analytical model includes classic classifiers, distinctive ensemble methods such as bagging, feature based ensemble, voting and stacking. The results show a superior prediction performance comparing with the related approaches.
Constacyclic Codes Over 𝑭𝒒[𝒖] /〈𝒖𝟑 = 𝟎〉 and Their Application of Constructing Quantum Codes

Let 𝑅 = 𝐹𝑞+u𝐹𝑞+𝑢^2𝐹𝑞, 𝑢^3=0 be a finite chain ring. In this paper, we give the structure of constacyclic codes over 𝑅 and obtain self-orthogonal codes over 𝐹𝑞 by using the Gray map from 𝑅𝑛 to 𝐹𝑞^(3𝑛). As an application, we present a construction of quantum codes from the codes obtained from this class.