Reviewerly/exHarmony huggingface dataset:
A Hybrid Optical-Electrical Analog Deep Learning Accelerator Using Incoherent Optical Signals

We present a hybrid optical-electrical analog deep learning (DL) accelerator, the first work to use incoherent optical signals for DL workloads. Incoherent optical designs are more attractive than coherent ones as the former can be more easily realized in practice. However, a significant challenge in analog DL accelerators, where multiply-accumulate operations are dominant, is that there is no known solution to perform accumulation using incoherent optical signals. We overcome this challenge by devising a hybrid approach: accumulation is done in the electrical domain, while multiplication is performed in the optical domain. The key technology enabler of our design is the transistor laser, which performs electrical-to-optical and optical-to-electrical conversions efficiently to tightly integrate electrical and optical devices into compact circuits. As such, our design fully realizes the ultra high-speed and high-energy-efficiency advantages of analog and optical computing. Our evaluation results using the MNIST benchmark show that our design achieves 2214× and 65× improvements in latency and energy, respectively, compared to a state-of-the-art memristor-based analog design.
Exploiting the security of N = prqs through approximation of ϕ(N)

This paper reports new techniques that exploit the security of the prime power moduli [Formula: see text] using continued fraction method. Our study shows that the key equation [Formula: see text] can be exploited using [Formula: see text] as good approximation of [Formula: see text]. This enables us to get [Formula: see text] from the convergents of the continued fractions expansion of [Formula: see text] where the bound of the private exponent is [Formula: see text] which leads to the polynomial time factorization of the moduli [Formula: see text]. We further report the polynomial time attacks that can break the security of the generalized prime power moduli [Formula: see text] using generalized system of equation of the form [Formula: see text] and [Formula: see text] by applying simultaneous Diophantine approximations and LLL algorithm techniques where [Formula: see text] and [Formula: see text].
A Trust Assisted Matrix Factorization based Improved Product Recommender System

Smart services is an efficient concept to provide services to the citizen in an efficient manner. The online shopping and recommender system play an important role in this scenario that provides efficient item recommendations to the citizens. Though, the majority of the latest recommender systems can't get effective and efficient prediction accuracy because of the sparsity of the item matrix against each user. Additionally, the recommendations are not reliable when tested upon larger datasets. To handle these problems, a trust-based technique is proposed, called trustasvd++, which fuses a user's trust data in the MF context. The offered strategy combines trust data and rating values to deal with the sparsity and cold start user's issues. Matrix Factorization (MF) has been recognized as a persuasive method for the formation of an effective Recommender System. Pearson correlation coefficient (PCC) is used as a similarity metric in the proposed technique. To assess the efficiency of the offered strategy, numerous datasets have been done on datasets including Epinions, Filmtrust, and Ciao.
Scientific and educational cluster as a center of public and private management of construction trades

The author's definition of applied qualifications is given. It is aimed at the formation of applied qualifications based on resource centers of secondary vocational education institutions that are part of the construction research and education cluster. The principles of organizational and pedagogical conditions of the relations of the resource center with enterprises within the context of public and private administration are identified. The results of the survey among students on the issue of whether they want to learn applied qualifications are given. The organizational and pedagogical conditions of the innovative development of the educational cluster are formulated.
Assessing Growth and Instability of Open Source Software Systems

Software systems progressively evolve over time where source code needs to be modified, newly are added and others need to be deleted. Therefore, it is worth to monitor progress and software development, to measure growth and assess system stability. This paper assesses OSS software growth and instability to clarify impacts of its instability on its system growth. Three open source software applications, with different characteristics, have been selected. Size and instability metrics are extracted and statistically analyzed in order to assess the reflections of software growth under instability behaviors.The correlation among size metrics are highly correlated - the highest and lowest correlation are 0.99 and 0.72 respectively. Moreover, the result shows the highest correlation is between total classes and total lines of code. The instability measures on software systems exhibits certain pattern characterized by more addition and modification than deletion at system design level and the implementation changes exhibits more addition than modification and deletion. although the characteristics of the open source applications are different than the closed-source, the Lehman's observations are still valid with regard to continuous growth and trends.
Estimation of the Monthly Standard Diffuse and Universal Solar Eradiation for the City Varanasi, Uttar Pradesh, India

Solar radiation data is important for supposing power capacity that can be set up from photovoltaic unit. Solar radiation tumbling on the earth’s exterior is consistent by an equipment called a total radiometer. This paper describes the process for the development of monthly standard universal and diffused radiation for the city Varanasi in Uttar Pradesh, India. It can be located at Longitude: 82.9738° E and Latitude: 25.3076° N at an elevation of 81 meters. The Gopinathan model has been used for the calculation of values of the monthly standard universal cosmic radiation. Gupta and Kreith’s models have been used for estimating the values of monthly standard diffuse cosmic radiation. The calculated data has been analyzed and the result has been simulated through MATLAB.
Work-in-Progress-The Use of Plane-Detection Augmented Reality in Learning Geometry

During the last decade, Augmented Reality (AR) has become popular technology in education. From a pedagogical perspective of mathematics education, AR is embodied by virtual manipulatives and matches well with constructivism. To support geometry learning based on constructivist learning theory, this work-in-progress paper aims to shed light on the pedagogical potentials and design principles of using plane-detection AR. Plane-detection AR, the marker-less AR technique, can significantly increase the degree and the scope of learners' interaction. Plane-detection AR can also bring physical objects into geometry learning in a meaningful way. This paper also provides the developed plane-detection AR mobile application as an example. The practical hurdles of using plane-detection AR, and proposed study design for future research to investigate the effects of using plane-detection AR are further discussed.
Two-photon state reconstruction with an adaptive tomographic framework

Quantum state tomography is a mathematical process to fully determine the elements of a density matrix of a quantum state with a complete basis-measurement. The requirement of these measurements multiplies exponentially with qubits, subsequently the data post-process for large qubit states becomes a big challenge to the memory of the computer. An efficient method is thus required to precisely determine the unknown states with fewer measurements. We propose an adaptive tomographic technique with which the measurement guides itself to converge to its own target state. This adaptive tomography directly tests the hypotheses by measuring the real-time correlations within an iterative algorithm, i.e., simulated annealing method (SAM). The simulation of tomography on two-photon state justifies the validity and robustness of this method. It outperforms traditional full tomography owning owing to its higher efficiency and higher accuracy.
Control System Design of Transport Robot Based on Multi-color Recognition

According to the rules and requirements of the handling project of the China Engineering Robot Contest, an intelligent handling robot with Freescale KL25 chip as the core controller and capable of automatically identifying multiple colors was designed. Based on the completion of the hardware circuit and mechanical structure, the robot’s trajectory on the field is planned through software programming. Establishing a handling strategy allows the robot to efficiently grasp, transport, and stack objects. The focus is on the infrared tracking module, the color recognition module software design and the robot path planning strategy research. After the completion of the robot construction, through experiments in various links, the total number of loops compared to the traditional handling scheme has increased by about 11.1%, and the running time has been reduced by about 41.5%. The effect is good.
Research on the Competence Literacy Model Construction of National Scholarship Winners in the Context of Big Data

The Internet can break through the limitations of time and space, and provide more possibilities for education in the context of the world's popular situation. In this article, the author uses big data to analyze the national scholarship evaluation criteria and the experience of ESE scholarship winners in recent years, combined with the core development literacy of Chinese students proposed in 2014, the author build on the basis of independent development and social participation Get up, take independent study, participate in management, practice, and undertake the four major achievements as the framework to realize the establishment of the theoretical model of the ability and literacy of the national scholarship recipients of the "double first-class" university. Through the construction of the model, it can provide corresponding practical ways for the cultivation of university talents.
Trustworthy digital infrastructure for identity systems: why should privacy matter to security engineers?

Privacy and security have long been perceived as competing objectives, but they both exist in a state of tension within systems. Engineers may be tempted to consider security as essential, while privacy is a nice-to-have. Prof Carsten Maple, Dr Gregory Epiphaniou and Mirko Bottarelli of the University of Warwick and the Alan Turing Institute explain that, as applications and platforms gather ever more data, we need to move to a ‘privacy by design’ approach while implementing data protection mechanisms with existing security engineering methods. Privacy and security have long been perceived as competing objectives. For example, it is widely acknowledged that national security is often (and should often be) prioritised over any concerns for personal privacy. However, privacy and security are, more generally, in tension within cyber systems. This is most noticeably present in the design and operation of systems where information that can be used to determine and protect the security might have private and personal aspects.
An efficient image tamper detection and self-recovery scheme using rehashing model based digital watermarking

Image tamper detection techniques have been proposed to address the potential tampering problems of image content, as digital images can be easily modified, diffused, transformed and duplicated via the Internet. In order to achieve high detection successful rate and self-recovery accuracy in image tamper areas, an efficient scheme of rehashing model based digital watermarking for image tamper detection and self-recovery is proposed in this study. The rehashing model consists of a series of random hash functions, which is presented to avoid numerous random number collisions that embrace the authentication data for image tamper detection and self-recovery. Additionally, these authentication data can be further embedded into the original image as digital watermarking by means of the constructed two-dimensional reference matrix. The experimental results demonstrate that this novel scheme outperforms the other related works in terms of tamper area localization and self-recovery accuracy; moreover, the proposed image tamper detection and self-recovery technique can obtain an acceptable recovery image visual quality with half image content tamper.
Analysis on Weighted Average Between Features in Dictionary Learning and Sparse Representation Algorithms for Low-Resolution Images

Recently, the problem of Low-Resolution (LR) is happened to be the key challenge in the field of image processing. To tackle this problem, Super-Resolution (SR) techniques have been developed. Generally, SR is used to acquire more information about an image by recovering an HR image from the LR image without losing high frequency details [1]. This paper is focused on analysing the effectiveness of using combined features in dictionary learning and sparse representation algorithms for producing images with better resolution. The method used to combine the properties of features: energy (F1) and entropy (F2) extracted in this paper is known as the weighted average techniques. In this case, different combination of weightage which was written as [W1, W2] will be assigned to F1 and F2 respectively. As a result, the weightage combination of [2, 8] achieved the highest improvement in PSNR values of 6.0863 dB and the second highest improvement in SSIM values of 0.2559. In conclusion, the SR system constructed based on the dictionary learning and sparse representation algorithms with the use of weighted average between features is able to solve the image LR problems. This work can be improved by testing on more input images obtained from databases.
Improving the Relevance of a Web Navigation Recommender System Using Categorization of Users' Experience

We propose a method for a recommender system for generating Web-navigation suggestions. The purpose of this system is to assist its users by providing them suggestions for possible desired next steps whenever they get stuck in using any software. We are able to achieve this goal by leveraging the principal of “crowd-sourcing”. Specifically, we leverage the crowd's knowledge under the assumption that there are cohesive groups of experienced and novice users. Therefore, we present an algorithm that measures the right heuristics in order to classify users by their experience, and then relates these users with association rules of web-navigation derived from frequent patterns mining. In this paper we introduce our method, compare it with other current solutions in the field, outline the proposed algorithm, and present an experiment which serves as our proof-of-concept.
Sensitive Data Recognition and Filtering Model of Webpage Content Based on Decision Tree Algorithm

In recent years, privacy-protecting data mining has attracted widespread concern because it is necessary to provide protection for the privacy level of sensitive and confidential data from unauthorized attacks. The purpose of this study is to develop a privacy-protecting anonymity algorithm using decision tree classification. This paper focuses on k-anonymity technology, which can prevent identity leakage. K-anonymity technology adopts generalization and suppression methods to achieve data anonymity. Then, the privacy level and mining quality of anonymous data sets will be tested by using decision tree classification, and then compared with other data mining technologies (logistic regression and support vector machine). As is shown in the research, compared with other data mining technologies, privacy level and data quality provide better results.
Use of Analytical Network Process Algorithm in the decision-making process

Abstract The aim of this research is to implement a decision support system where the resulting system is in the form of a recommendation for existing outcomes. Conducting research to select sunblock products. Sunblock is one of the lotions that must be used before leaving the house so that the skin can be protected from sunlight and UV rays, UV rays can have a harmful effect on the skin, therefore the skin needs sunlight protection, one of which is by using a sublock, many sunblock products on the market. The aim of this study is therefore to find the best sunblock products based on the consumer experience of sunblock users. The data obtained to complete the study were taken from the results of questionnaires distributed to sunblock users randomly located in the Simalungun district. This study uses the ANP (Analytic Network Process) decision support system, the researcher uses several criteria to complete this study, including: (H) Price, (Jk) Skin Type, (Ef) Side Effects, (Kp) Product Quality, (Ap) The rules of use, (Ktp) Product affordability and alternatives used are: (A1) Nivea, (A2) Vaseline, (A3) Wardah. The results of the study using the ANP method based on a questionnaire distributed to consumers using sunblock showed that the best recommendations for sunblock type recommended by consumers were Nivea (A1) = 7.2, Vaseline (A2) = 5.4 and Wardah (A3) = 5.07.
Research on YOLO Model and Its Application in Fault Status Recognition of Freight Trains

Compared with other object detection and recognition methods, YOLO is an end-to-end algorithm that integrates target region prediction and target category prediction into a single neural network model. It can realize rapid target detection and recognition with high accuracy, which is more suitable for field application. The network models of YOLOV3 and YOLOV4 are studied, aiming at the application environment of fault state detection in railway transportation and freight. In view of the shortcomings of YOLOV3 algorithm, the Dense block-BC is used to replace Darknet-53 to build the YOLOV3 backbone network. CIoU is used to replace the MSE to calculate the boundary box loss, and Focal loss is used to redesign the loss function to improve the YOLOV3 model. The improved YOLOV3 and YOLOV4 are respectively used to detect the fault state in railway locomotive transportation, and the better detection results are obtained.16,660 pictures are collected directly from the railway transportation site as train set, and other 3,000 pictures are used as test set. The omission factor can be reduced to 4.164%, and the error rate can be decreased to 4.218%.The detection speed of each picture is 0.4s, which can meet the requirements of non-stopping fault state detection in railway transportation.
Dynamic Reusability Measurement Using Machine Learning Algorithms in Object-Oriented Environment

Software reusability is measured with the help of object-oriented static and dynamic metrics to better predict the quality of software effectively. Most of the research exists in the literature to measure the reusability of software based on static metrics by capturing the design patterns that do not take a count on run time behaviour of object-oriented software. Therefore, in this paper, authors derived a new approach to measure the software reusability of a design pattern based on dynamic metrics that capture the run time features of object-oriented programming, i.e., run time polymorphism, dynamic binding, etc. Besides, dynamic metrics were extracted by using AspectJ, an implementation of aspect-oriented programming on the eclipse platform. Further, authors proposed a fuzzy logic model based on static and dynamic metrics to measure reusability factor of a design pattern and compared it with various other existing machine learning algorithms such as linear regression (LR), Gaussian processes (GP), simple linear regression (SLR), SMOreg, random forest (RF) and M5P. In the end, an experimental study was done on 18 design patterns, and it is concluded that proposed fuzzy logic approach gives satisfactory results based on dynamic metrics to better predict the reusability factor of a design pattern.
Research on Secret Sharing Scheme in CPS Environment

With the rapid development of Cyber-Physical System (CPS) technology, the security requirements of data communication in CPS environment are constantly improving. Data communication systems usually use encryption technology to encrypt information. The core of encryption is the security of key management. The traditional key management method is to back up the key repeatedly and store it in different places, but it still cannot fundamentally ensure the security and confidentiality of information. Using secret sharing technology can solve the above problems. The so-called secret sharing system means that the secret S is provided to all members of a participant set for separate storage. Only when all members of an authorized subset in set P show their secret sharing, the shared secret S can be recovered, and any member of the unauthorized subset cannot get any information secret. This paper first introduces the architecture and technology of CPS and analyzes the security threats of CPS. After studying the typical secret sharing scheme, this paper analyzes the security of the secret sharing scheme and the prevention measures of sharing fraud in detail.
Technology of Formation of Cognitive Logical Universal Educational Actions by Means of the Subject "Chemistry" in the Context of the Implementation of the Federal State Educational Standard of Secondary (Complete) General Education

Within the framework of the article, the technology of formation of cognitive logical universal educational actions (CLUEA) by means of the subject “Chemistry” in the context of the implementation of the Federal State Educational Standards of Social Research. The conditions for using this technology in the educational process are considered and possible risks are described. The results of the author's use of the technology for the formation of cognitive logical universal educational actions (CLUEA) in the course of the subject “Chemistry” in the context of the implementation of the Federal State Educational Standards of Secondary (Complete) General Education. The conclusions about the effectiveness of the technology used, taking into account the age of students and the type of educational institution are given.
The Undecidability of System F Typability and Type Checking for Reductionists

The undecidability of both typability and type checking for System F (polymorphic lambda-calculus) was established by Wells in the 1990s. For type checking Wells gave an astonishingly simple reduction from semi-unification (first-order unification combined with first-order matching). For typability Wells developed an intricate calculus to control the shape of type assumptions across type derivations via term structure. This calculus of invariant type assumptions allows for a reduction from type checking to typability. Unfortunately, this approach relies on heavy machinery that complicates surveyability of the overall argument. The present work gives comparatively simple, direct reduction from semi-unification to System F typability. The key observation is as follows: in the existential setting of typability, it suffices to consider some specific (but not all, as for invariant type assumptions) type derivations. Additionally, the particular result requires only to consider closed types without nested quantification. The undecidability of type checking is obtained via a folklore reduction from typability. Profiting from its smaller footprint, correctness of the new approach is witnessed by a mechanization in the Coq proof assistant. The mechanization is incorporated into the existing Coq library of undecidability proofs. For free, the library provides constructive, mechanically verified many-one reductions from Turing machine halting to both System F typability and System F type checking.