Reviewerly/exHarmony huggingface dataset:
Smart Underwriting - A Personalised Virtual Agent

In life, unplanned expenses are a bitter truth. Insurance policies therefore offer a semblance of support to minimize financial liability from unforeseen occurrences. The world which has faced major multiple lockdowns owing to the pandemic has certainly started thinking of ways to work online and hence reinforce the need to rethink underwriting. Traditionally, information of the client who needs insurance is sent to the underwriter through an email and after proper analysis, the underwriter sends the quick quote back to the agent based on his intuition and experience. The current process of insurance claimants is conservative, extremely tedious and time consuming. In the era of Artificial Intelligence, an individual prefers the task to be done in an efficient & less dreary manner, so rather than navigating through the website or visiting the Office in person, it would prefer an application that provides a unified platform to manage their policies. Hence chatbot is a much-optimized solution for automating the traditional underwriting process. By combining the fields of Natural Language Processing, Facial recognition, Optical Character Recognition and Blockchain, the chatbot would provide solutions for all policy related queries and operations.
The Bias-variance Decomposition in Profiled Attacks Based on Deep Learning

Side channel analysis/attack(SCA) is one of the main security threats to cryptographic devices. The attacker analyzes the leaked information during the physical realization of the cryptographic algorithm, and then recovers the key information. This poses a serious threat to the security of cryptographic algorithms. In recent years, with the continuous development of deep learning technology, profiled side-channel attacks based on deep learning technology pose severe challenges to the security of encryption devices. For bias-variance decomposition is an effective tool for evaluating the prediction errors of deep neural network models, this paper proposes a deep learning model evaluation method based on bias-variance decomposition, witch divides the performance of side-channel attack models based on deep neural network into four levels. This paper analyzes the performance of the deep neural network model used for side-channel attacks through experiments, and then realizes parameter optimization and model performance evaluation, so as to better evaluate the performance of the deep neural network model used for side-channel attacks.
APPLICATION OF COMPREHENSIVE FUZZY EVALUATION BASED ON STUDENT PARTICIPATION IN GENERAL EDUCATION COURSE EXAMINATION

In view of the assessment methods of general education courses, an assessment and evaluation model is constructed, which consists of the usual results, material learning, cooperative discussion and classroom performance, and the final results, which are combined with on-site assessment and paper assessment. Introducing students to participate in the process assessment and on-site assessment of general education courses innovatively improves students' enthusiasm to participate in the general education class. In the process of empirical research, the order relation analysis method was firstly used to judge the importance of "homework summary, material learning, cooperative discussion, classroom performance ", etc., and then the weight coefficient was determined. Then the comprehensive evaluation method based on fuzzy mathematics was adopted. Homework summary, material learning, cooperative discussion, classroom performance review and field is not so easy to use, such as the exact scores show with the method of fuzzy evaluation, the process of fully reflect the students' learning process of a curriculum, and intensify students participate in class, let students participate in curriculum evaluation, can more active classroom and improve the effect of course.
Research of Immersion English Teaching on the Multi-dimensional Training Strategy for College Students

College students have many problems in English learning. For example, education is only at the language level, and students are not trained in multiple dimensions, which has some side effects on the better growth and development for students. The study of immersion English teaching can improve college students' English learning dimensions, cultivate students in many aspects and enhance their knowledge of English. This paper briefly analyzes the obstacles faced by college students in English learning, and simply expounds the relevant knowledge of immersion English teaching. And this paper focuses on the strategies of multi-dimensional cultivation of college students by immersion English teaching.
Qualimetric Assessment of Pedagogical Factors in the Formation of Professional Environmental and Economic Competence of University Graduates

The current situation of green economy digitalization actualizes the problem of developing innovative principles of professional training in universities. The key conditions for the training of highly qualified personnel in accordance with the needs of society and employers as participants of educational relations are of particular importance due to the qualimetric assessment of educational achievements of students (QAA), which has been actualized since 2000 by conceptual research within the framework of the Programme for International Student Assessment (PISA). The material for the study was modern approaches, algorithms and models of QAA in higher education institutions. The main methods of research were theoretical analysis of published scientific literature on the problem of digital assessment of the level of professional competence of university graduates in the field of green economy-sustainable development, primarily, their professional environmental and economic competence (PEEC) based on a ranking analysis of expert information on the impact of different factors. At the same time, the problems which are the result of multifactor dependencies typical for pedagogy are put into the structured category and the apparatus of mathematical modelling and selection of optimal solutions is scientifically grounded to solve them. The most significant pedagogical factors were selected according to their influence on the level of PEEC formation as a result of using the method of expert group evaluation and ranking the selected opinions of experts, the consistency of which was previously revealed by calculating the Kendall's concordance coefficient. The principles of the systematic approach applied in the pedagogical research, using the conceptual provisions of quantitative measurements, numerical modelling and mathematical statistics based on factor planning allowed us to present the results of the experiment to identify the final dependence of the level of PEEC formation as a regression model as the initial step in the developed platform (underlying concept) of qualimetric assessment of the dominant factors of PEEC formation. The initial principles of such a concept are formulated, providing opportunities for studying and analyzing the influence of various pedagogical factors and organizational and pedagogical conditions, choosing the ways of generating educational trajectories.
On Validity of Extreme Value Theory-Based Parametric Models for Out-of-Distribution Detection

Open-set classifiers need to be able to recognize inputs that are unlike the training or known data. As this problem, known as out-of-distribution (OoD) detection, is non-trivial, a number of methods to do this have been proposed. These methods are mostly heuristic, with no clear consensus in the literature as to which should be used in specific OoD detection tasks. In this work, we focus on a recently proposed, yet popular, Extreme Value Machine (EVM) algorithm. The method is unique as it uses parametric models of class inclusion, justified by the Extreme Value Theory, and as such is deemed superior to heuristic methods. However, we demonstrated a number of open-set text and image recognition tasks, in which the EVM was outperformed by simple heuristics. We explain this by showing that the parametric (Weibull) model in EVM is not appropriate in many real datasets, which is due to unsatisfied assumptions of the Extreme Value Theorem. Hence we argue that the EVM should be considered another heuristic method.
MultiCall: A Transaction-batching Interpreter for Ethereum

Smart contracts are self-executing programs running in the blockchain allowing for decentralised storage and execution without a middleman. On-chain execution is expensive, with miners charging fees for distributed execution according to a cost model defined in the protocol. In particular, transactions have a high fixed cost. In this paper we present MultiCall, an interpreter that reduces the cost of smart contract execution by emulating sequences of transactions from multiple users in one transaction. We have implemented and integrated MultiCall into Ethereum. Our evaluation shows that using MultiCall provides a saving between 56.8% and 98.9% of the fixed per-transaction cost compared to the standard approach of sending transactions individually.
Application of Excel in Leveling and Traverse Survey

Abstract It mainly discusses the application of Excel in field leveling and traverse adjustment calculation in this thesis. Excel has powerful functions of formula editing and function, so we use it to process survey data. We realize the advantages of Excel software in the measurement data processing. It is convenient, flexible, simple, and clear. A good Excel calculation table can be used in future work. It only needs to adjust the original data without repeating the arrangement and design of calculation formulas. It can completely replace the programmable calculator and bring a lot of convenience to the work.
Deep Collaborative Conjunctive Recommender Model based on Review Rating Prediction

Ratings or Recommendations provided by the users are a valid note & a significant reference to permissible users.However, these ratings are most probably biased or non-predictable in nature. This paper provides a Machine Learning based model to retrieve, analyze and predict a model to implement the user ratings and suggest a recommendation systems based on the user interests and necessity. Rating or recommendation models deploying the latest algorithms are a necessity on the recent times, wherein the markets rely undoubtedly on the reviews of the experienced users and customers. Significant issue rises in the classification of the dataset and analyzing and recommending the same. This paper implements the same based on the movie recommendation
A comparative study of energy and task efficient load balancing algorithms in cloud computing

Abstract The future growth of the Internet of Services has fundamentally changed the emergence of cloud computing. Cloud data centres serve multiple tenant demands for cloud applications that discharge vast amounts of electricity, leading to high operating costs and environmental diffusion of carbon dioxide (CO2). To fix this is the need for preservation to enable potential use by building a new structure and measuring the effect in a cloud data centre. Consequently, the use of pruned electricity reduces the cost of processing power. In order to meet energy-efficient data centres in the cloud, adjusting to optimal load balancing processing a good way for energy savings. To minimise the large energy use of cloud data centres herds, this focuses on increasing efficacy by breaking the workload evenly. In this paper, we plan to provide a comprehensive comparative analysis in cloud computing of current load balancing algorithms.
How do warnings affect retail demand for Bitcoin? Evidence from an international survey experiment

Bitcoin is associated with different risks. We conduct an information experiment in the four largest European economies to analyze the effects of specific warnings and information on retail investors’ demand for Bitcoin. Our results indicate that the impact is strongest when warnings point to privacy issues. Information on the lack of guarantees or on CO2 emissions only affects particular subgroups. Knowledge of broad public acceptance increases overall demand. Warnings can, therefore, effectively prevent extreme market events while avoiding the costs of stricter regulation. Effect heterogeneity implies that regulators should use specific information and different communication channels to reach relevant investors.
Efficient Fine-Grained Visual-Text Search Using Adversarially-Learned Hash Codes

Cross-modal hashing for efficient visual-text search has attracted much research enthusiasm in recent years. The main argument of this work is that existing hashing methods mainly exploit a multi-label matching paradigm, ignoring various fine-grained semantics (high-order relationships, object attributes, etc.) in the multi-modal data. This paper explores cross-modal hashing from two rarely-explored aspects: first, we propose an efficient two-step hashing scheme that quickly screens irrelevant samples with global feature and then generate fine-grained feature guided by high-order concepts to re-rank the survived candidates. Secondly, the robustness of the cross-modal hashing model, particularly under subtle tampering of fine-grained queries, is formally investigated. We propose a rephrase and adversarial training strategy for obtaining better performance and robustness. Comprehensive experiments and ablation studies on two large public datasets (MS-COCO and Flickr30K) demonstrate the proposed method’s superiority in terms of both efficiency and accuracy.
A Cloud Computing Capability Model for Large-Scale Semantic Annotation

Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for these challenges. To achieve this, a set of requirements towards holistic semantic annotation on the web is defined and mapped with cloud computing mechanisms to facilitate them. Technical specification for the requirements is critically reviewed and examined against each of the cloud computing mechanisms, in relation to their technical functionalities. Hence, a mapping is established if the cloud computing mechanism's functionalities proffer a solution for implementation of a requirement's technical specification. The result is a cloud computing capability model for holistic semantic annotation which presents an approach towards delivering large-scale semantic annotation on the web via a cloud platform.
Privacy Protection Method for Experts' Evaluation Ability Calculation of Peer Review

Most of the existing calculation method of expert evaluation ability directly call data onto calculation, which leads to the risk of privacy leakage of expert review information and affects the peer review environment. With regard to this problem, a privacy protection method of experts' evaluation ability calculation of peer review is proposed. Privacy protection and data usability are adjusted according to privacy preferences. Using Gauss distribution and combining with the distributive law of real evaluation data, the virtual projects are generated, and the project data are anonymized according to the virtual projects. Laplace distribution is used to add noise to the evaluation sub score for perturbation, and the evaluation data are obfuscation according to the perturbation sub score. Based on the protected project data and evaluation data, the expert evaluation ability is calculated, and the review privacy is protected. The experimental results show that the proposed method can effectively balance the privacy protection and the accuracy of the calculation results.
The development of the system of higher education for women in the United States (the second half of the XIX century)

The XIX century is the era of fundamental change in the status of American women, which marks non only transformation of the attitude towards woman as an equal subject of social and family relations, but also their pursuit of self-actualization. Such changes among the middle-class American women could not have happened without receiving higher education &amp;ndash; as the opportunity for employment and increase of self-consciousness. Special attention is given to the emergence and development of higher education institutions since the first instance of admission to the establishment of the system of education of the American women. The conclusion is made that the changes that took place in the U.S. society after the Civil War and during the industrialization era contributed to the change in the status of American women in the second half of the XIX century. This is the time of establishment of multiple colleges for women; private and public coeducation institutions opened their doors to American women; and the myth of the negative impact of education upon women's health was shattered. Generalization of the foreign sources and research demonstrated the difficulties faced by women during their college and university years, as well as the transformation of attitude of the American society towards the changing status of women with regards to receiving higher education.
Rank and select operations on a word

Given a bit-vector t, operation rank1(t,i) returns the number of occurrences of 1-bits in the prefix of t ending at position i. Operation select1(t,c),c≥1 returns the position of the c-th 1-bit in t. These operations are building blocks for succinct data structures. We present algorithms of rank and select on an arbitrary word that run in O(log⁎⁡w) time and use constant space by basic operations on words, where w is the word size in bits of the word RAM model. The method improves the current known O(log⁡log⁡w) method of counting the number of 1-bits in a word. We also give rank and select algorithms taking constant running time and space for bit-vectors of length not greater than w/(⌈lg⁡w−lg⁡lg⁡w⌉+1).
Solar Radiation Estimation Using Discriminate Analysis Algorithm for The Output Voltage of Photovoltaic Module

Abstract A study of solar radiation in one area is very important to decide the area is suitable or not to be constructed the generation of photovoltaic (PV) system. The solar radiation can be recorded using the equipment of solar power meter or weather station. But it is due to the limitation of equipment, maybe a technical error occurs on the equipment, thus the equipment has a problem to record the data of solar radiation or one area has no equipment of solar radiation recording. An estimation method of solar radiation is needed to solve the problem. This paper presents an estimation of solar radiation using discriminate analysis algorithm (DAA) that it is applied to the output voltage of PV module. The data of daily measured solar radiation, maximum and minimum temperature are obtained from Chuping Weather Station through the web of world weather online for the year of 2019. The maximum and minimum temperature are as predictor variables in the DAA to obtain the daily estimated solar radiation. The result of daily estimated solar radiation is applied to the output voltage of PV module and compared to the output voltage of PV module applied by the daily measured solar radiation using the statistical analysis, they are root mean square error (RMSE) and error percentage. The results show that the estimated and measured solar radiation have RMSE of 0.257 and error percentage of 1.649%. They indicate that the DAA is acceptable to estimate the solar radiation.
An Optimal Binary Particle Swarm Optimization Based Feature Selection Model for Big Data Analysis of Product Assessment

Big data defines the state where the size, speed and kind of data go beyond a memory or executing capabilities for precise and timely decision-making. Big data analytics is integrated with ML and statistical methods for processing big data and recognizes the important data. At present times, the generation of online product reviews has exponentially increased at each and every second. These applications have resulted in developing the volumes of data which can be used for prediction and classification for decision making process. Compared with other models, various techniques are applied in solving the big data problem, feature selection (FS) is known to be an efficient method. FS operations could be exploring with the application of a subset of features which is related to the topic of précised definition of the existing datasets. Deplorably, search using this type of sub-sets results in the problems of combinatorial as well as maximum time consuming. The meta-heuristic approaches are typically employed to facilitate the choice of features. This paper presents an optimal extreme learning machine (ELM) based binary particle swarm optimization to precede the FS process. The proposed method develops a Fitness Function (FF) by applying ELM. And the best solution of the FF has been explored under the application of BPSO technique. For instance, the dataset of product review which are derived from Amazon including synthetic data, which is comprised with total of 235,000 positive and 147,000 negative review records is used. The experimental result implied that the ELM-BPSO technique is comparably best
ILSSPP: Intelligent location sharing system with privacy preserving features for smart cities

Social networking services are increasingly accessed through mobile devices. This trend has prompted services such as Facebook and Google+to incorporate location as a de facto feature of user interaction. At the same time, services based on location such as Foursquare and Shopkick are also growing as smartphone market penetration increases. In fact, this growth is happening despite concerns (growing at a similar pace) about security and third-party use of private location information (e.g., for advertising). Nevertheless, service providers have been unwilling to build truly private systems in which they do not have access to location information. In this paper, we describe an architecture and a trial implementation of a privacy-preserving location sharing system called ILSSPP. The system protects location information from the service provider and yet enables fine grained location-sharing. One main feature of the system is to protect an individual’s social network structure. The pattern of location sharing preferences towards contacts can reveal this structure without any knowledge of the locations themselves. ILSSPP protects locations sharing preferences through protocol unification and masking. ILSSPP has been implemented as a standalone solution, but the technology can also be integrated into location-based services to enhance privacy.