Reviewerly/exHarmony huggingface dataset:
Target Intention Reasoning Algorithm Based on Multi-agent Network and Adaptive Genetic Bee Colony

Aiming at the problem that the analysis of target intention is difficult to apply to real environment and the reasoning efficiency is low, this paper constructs a target intention reasoning algorithm based on multi-agent network, which represents the target information and the relationship between them. Firstly, the problem of target intention inference is transformed into the problem of optimal inference path search in complex networks. Secondly, the idea of genetic mutation crossover is introduced into the artificial bee colony algorithm to improve the food source adaptation strategy and improve the solving ability and convergence speed of the global optimal solution. Finally, the experimental results show that the proposed method is significantly improved in reasoning accuracy and average time consumption compared with genetic algorithm and artificial bee colony algorithm.
Sugarcoating a Bitter Pill - VR Against Police Ethnic Profiling

Ethnic profiling is a growing concern for police organizations. However, attempts to reduce possible biases toward certain demographic groups have shown little effect, possibly because of the unpopularity of the topic. This study was set up to explore whether a dedicated VR training may contribute to officers’ knowledge, attitude and willingness to communication about ethnic profiling. This was done by comparing effects of the VR training with that of the same training presented in 2D with a smart phone, and a control condition in which no training content was administered. Although no results were found for attitude and communication, the results do show some consistent results regarding knowledge in favour of VR. In addition, VR proved to result in higher ratings of presence, engagement, and enjoyment, indicating that in addition to providing more persuasive content, another strong advantage of VR as part of a training is the experience it affords.
ReadAR, Playful Book Finding Through Peer Book Reviews for Multi-faceted Characters in AR

One important element to provide reading enjoyment and to persuade children to read (more) is providing children with books that fit their interests. We structure filtering and recommendation of books in a playful way via animated 3D characters. These characters have an unusual mix of characteristics that can be related to categories of books, while at the same time aiming for overcoming a filter bubble effect. In our Augmented Reality application the characters playfully ‘structure’ the process of book review and searching. We tested the prototype during two within-subject sessions, testing reflecting on the book as well as searching for books, with respectively 18 and 15 participants. When comparing to a regular ‘writing a book report’-approach, children indicated they would more likely want to use the app again for providing feedback about the book to peers as well as for finding books. Although they wanted to look again for the books and watch the accompanying localised video reviews from their peers, almost half did not want to record videos themselves again which points out a clear challenge for future improvements.
Enhancing Knowledge Graph Attention by Temporal Modeling for Entity Alignment with Sparse Seeds

As a fundamental task of knowledge graph integration, entity alignment (EA) matches equivalent entities across knowledge graphs (KGs). Temporal knowledge graphs (TKGs) enhance static KGs with temporal information. Traditional EA approaches tackle alignments of static KGs, but cannot effectively deal with TKGs. Therefore, temporal EA solutions are called for. To this end, we propose a time-aware graph attention network for entity alignment (TGA-EA). Generally, we learn high-quality temporal-relational entity embeddings for alignments by systematically integrating temporal information into KG embeddings. We propose three temporal modeling methods to effectively represent and integrate temporal information into both entities and relations. Then we construct temporal enhanced graph attention to produce target temporal-relational entity embeddings with temporal entities and temporal relations. Thanks to the powerful design, TGA-EA achieves promising performances with sparse alignment seeds. Extensive experiments on five datasets demonstrate our approach's obvious advantages over previous works.
Zero-Knowledge Arguments for Subverted RSA Groups

This work investigates zero-knowledge protocols in subverted RSA groups where the prover can choose the modulus and where the verifier does not know the group order. We introduce a novel technique for extracting the witness from a general homomorphism over a group of unknown order that does not require parallel repetitions. We then present a NIZK range proof for general homomorphisms as Paillier encryptions in the designated verifier model that works under a subverted setup. The key ingredient of our proof is a constant sized NIZK proof of knowledge for a plaintext. Security is proven in the ROM assuming an IND-CPA additively homomorphic encryption scheme. The verifier’s public key can be maliciously generated and is reusable and linear in the number of proofs to be verified.
Threshold Private Set Intersection with Better Communication Complexity

Given $$\ell $$ parties with sets $$X_1, \dots , X_\ell $$ of size n, we would like to securely compute the intersection $$\cap _{i=1}^\ell X_i$$ , if it is larger than $$n-t$$ for some threshold t, without revealing any other additional information. It has previously been shown (Ghosh and Simkin, Crypto 2019) that this function can be securely computed with a communication complexity that only depends on t and in particular does not depend on n. For small values of t, this results in protocols that have a communication complexity that is sublinear in the size of the inputs. Current protocols either rely on fully homomorphic encryption or have an at least quadratic dependency on the parameter t. In this work, we construct protocols with a quasilinear dependency on t from simple assumptions like additively homomorphic encryption and oblivious transfer. All existing approaches, including ours, rely on protocols for computing a single bit, which indicates whether the intersection is larger than $$n-t$$ without actually computing it. Our key technical contribution, which may be of independent interest, takes any such protocol with secret shared outputs and communication complexity $$\mathcal {O}(\lambda \ell {{\,\textrm{poly}\,}}(t))$$ , where $$\lambda $$ is the security parameter, and transforms it into a protocol with communication complexity $$\mathcal {O}(\lambda ^2 \ell t {{\,\textrm{polylog}\,}}(t))$$ .
Credibility in Private Set Membership

A private set membership (PSM) protocol allows a “receiver” to learn whether its input x is contained in a large database $$\textsf{DB} $$ held by a “sender”. In this work, we define and construct credible private set membership (C-PSM) protocols: in addition to the conventional notions of privacy, C-PSM provides a soundness guarantee that it is hard for a sender (that does not know x) to convince the receiver that $$x \in \textsf{DB} $$ . Furthermore, the communication complexity must be logarithmic in the size of $$\textsf{DB} $$ . We provide 2-round (i.e., round-optimal) C-PSM constructions based on standard assumptions: As an application, our protocols can be used to build enhanced round-optimal leaked password notification services, where unlike existing solutions, a dubious sender cannot fool a receiver into changing its password.
On Homomorphic Secret Sharing from Polynomial-Modulus LWE

Homomorphic secret sharing (HSS) is a form of secret sharing that supports the local evaluation of functions on the shares, with applications to multi-server private information retrieval, secure computation, and more. Insisting on additive reconstruction, all known instantiations of HSS from “Learning with Error (LWE)”-type assumptions either have to rely on LWE with superpolynomial modulus, come with non-negligible error probability, and/or have to perform expensive ciphertext multiplications, resulting in bad concrete efficiency. In this work, we present a new 2-party local share conversion procedure, which allows to locally convert noise encoded shares to non-noise plaintext shares such that the parties can detect whenever a (potential) error occurs and in that case resort to an alternative conversion procedure. Building on this technique, we present the first HSS for branching programs from (Ring-)LWE with polynomial input share size which can make use of the efficient multiplication procedure of Boyle et al. (Eurocrypt 2019) and has no correctness error. Our construction comes at the cost of a – on expectation – slightly increased output share size (which is insignificant compared to the input share size) and a more involved reconstruction procedure. More concretely, we show that in the setting of 2-server private information retrieval we can choose ciphertext sizes of only a quarter of the size of the scheme of Boyle et al. at essentially no extra cost.
Fine-Grained Verifier NIZK and Its Applications

In this paper, we propose a new type of non-interactive zero-knowledge (NIZK), called Fine-grained Verifier NIZK (FV-NIZK), which provides more flexible and more fine-grained verifiability of proofs than standard NIZK that supports public verifiability and designated-verifier NIZK (DV-NIZK) that supports private verifiability. FV-NIZK has two statistically equivalent verification approaches: We require unbounded simulation soundness (USS) of FV-NIZK to hold, even if an adversary obtains derived secret keys $$sk_d$$ with d of its choices, and define proof pseudorandomness which stipulates the pseudorandomness of proofs for adversaries that are not given any secret key. We present two instantiations of FV-NIZK for linear subspace languages, based on the matrix decisional Diffie-Hellman (MDDH) assumption. One of the FV-NIZK instantiations is pairing-free and achieves almost tight USS and proof pseudorandomness. We illustrate the usefulness of FV-NIZK by showing two applications and obtain the following pairing-free schemes:
Private Polynomial Commitments and Applications to MPC

Polynomial commitment schemes allow a prover to commit to a polynomial and later reveal the evaluation of the polynomial on an arbitrary point along with proof of validity. This object is central in the design of many cryptographic schemes such as zero-knowledge proofs and verifiable secret sharing. In the standard definition, the polynomial is known to the prover whereas the evaluation points are not private. In this paper, we put forward the notion of private polynomial commitments that capture additional privacy guarantees, where the evaluation points are hidden from the verifier while the polynomial is hidden from both. We provide concretely efficient constructions that allow simultaneously batch the verification of many evaluations with a small additive overhead. As an application, we design a new concretely efficient multi-party private set-intersection with malicious security and improved asymptotic communication and space complexities. We demonstrate the concrete efficiency of our construction via an implementation. Our scheme can prove $$2^{10}$$ evaluations of a private polynomial of degree $$2^{10}$$ in 157 s. The proof size is only 169 KB and the verification time is 11.8 s. Moreover, we also implemented the multi-party private set intersection protocol and scale it to 1000 parties (which has not been shown before). The total running time for $$2^{14}$$ elements per party is 2,410 s. While existing protocols offer better computational complexity, our scheme offers significantly smaller communication and better scalability (in the number of parties) owing to better memory usage.
Discretization Error Reduction for High Precision Torus Fully Homomorphic Encryption

In recent history of fully homomorphic encryption, bootstrapping has been actively studied throughout many HE schemes. As bootstrapping is an essential process to transform somewhat homomorphic encryption schemes into fully homomorphic, enhancing its performance is one of the key factors of improving the utility of homomorphic encryption. In this paper, we propose an extended bootstrapping for TFHE, which we name it by $$\textsf{EBS}$$ . One of the main drawback of TFHE bootstrapping was that the precision of bootstrapping is mainly decided by the polynomial dimension N. Thus if one wants to bootstrap with high precision, one must enlarge N, or take alternative method. Our $$\textsf{EBS}$$ enables to use small N for parameter selection, but to bootstrap in higher dimension to keep high precision. Moreover, it can be easily parallelized for faster computation. Also, the $$\textsf{EBS}$$ can be easily adapted to other known variants of TFHE bootstrappings based on the original bootstrapping algorithm. We implement our $$\textsf{EBS}$$ along with the full domain bootstrapping methods known ( $$\textsf{FDFB}$$ , $$\textsf{TOTA}$$ , $$\textsf{Comp}$$ ), and show how much our $$\textsf{EBS}$$ can improve the precision for those bootstrapping methods. We provide experimental results and thorough analysis with our $$\textsf{EBS}$$ , and show that $$\textsf{EBS}$$ is capable of bootstrapping with high precision even with small N, thus small key size, and small complexity than selecting large N by birth.
Digital Audit Platform Based on Visual Data Analysis

With the continuous change of technology in the new era, digitization is sweeping China and even the world. Enterprises generate various types of data in business innovation and upgrading, resulting in data gradually becoming a key production factor for enterprise development. As the supervisor of an enterprise, internal audit should take the initiative to grasp and lead the trend of digital technology transformation in the face of increasing audit data, and carry out digital audit transformation. The main purpose of this paper is to conduct research on the construction of a digital audit platform based on visual data analysis. This paper mainly focuses on the system construction goal of “enabling business and improving management efficiency”, in accordance with the work idea of “planning as the guide, demand-oriented”, and facing the needs of different audit users and work management, to build a system with “open, integrated, A digital audit platform with dynamic and intelligent features. The digital audit platform has achieved full coverage of State Grid’s main business audit supervision, and the level of structure, data and intelligence has been continuously improved, providing strong system support for digital auditing.
Dimensional Reduction of Rock Hyperspectral Signatures and Classification Based on a NCA Method used in developing a UAV Multispectral Imaging System

The adoption of hyperspectral imaging has had positive feedback in multiple industries, especially those heavily reliant on the visual analysis of subjects. Reasons for such are primarily due to the high accuracies achievable from processing high dimensional data. Nevertheless, hyperspectral data is said to possess a ‘dimensionality curse’. This phenomenon, deems it computationally demanding and difficult to employ in rapid field investigations such as the use of drone-mounted spectral cameras to distinguish rocks. To counter this, this study proposes the employment of a method of reducing the number of dimensions used to highlight the most characteristic feature bands referred to as Neighbourhood Component Analysis(NCA). NCA aided in disregarding redundant bands from 204 dimensionalities, to a still highly capable 5 bands dimensionality, which coincides with the current production of 5-band detection drones. To process this data, several machine learning(ML) algorithms were run in order to perform spectral classification of rocks based on the 5 NCA defined bands. This study’s novel findings show that one is able to acquire with NCA and ML, 5 bands, with a post-optimization average global accuracy of 95.4%. Such capabilities are highly sufficient considering the magnitude of the dimensionality reduction combined with the potential field drone applicability.
Sentiment Analysis Using Computer-Assisted Text Analysis Tools

Recently the use of computerized text analysis tools to assess an individual's linguistic, emotional and psychological characteristics has exploded in the field of empirical psychology.As a result, information about what people convey through their words can be swiftly and reliably extracted and analyzed.The key purpose of this research work is to analyze text data to assess linguistic and emotional characteristics with the help of computer-assisted text analysis tools.The analysis employed widely available text and sentiment analysis tools, Empath and LIWC.As text data, children's storybook reviews were analyzed in this work.These reviews are written by the children for the children.Empath and LIWC tools helped to measure the reviewer's sentiment, analytical ability and cognition level.Finally, by calculating the Pearson correlation coefficient for the selected variables, it is inferred that Empath shares a high correlation with LIWC.
Extractive Manuscript Summarization of Motivational Blogs in Natural Language Processing

The growing use of search engines business surveys, market updates, news reports, patients medical records etc., the use of automatic manuscript summarization is becoming more and more popular.In this process, the original data is compressed without changing its meaning.It is a growing field of Natural Language Processing; which is a branch of linguistics, computer science, and artificial intelligence.It studies how computers and human language interact, with a focus on how to design computers to process and analyze massive volumes of natural language data.The NLP also provides users to get valuable knowledge with one click.It is a very beneficial process, particularly when dealing with large article and source manuscripts.It provides the meaningful summary with-in a fraction of seconds, and it is a systematic way to manage large amount of data available on search engines, otherwise, it becomes very tedious for managing and reading.Summarize Bot, Resoomer, SMMRY, Text Summarization and Text Compactor, are the top five online tools for text summarization, among these all tools, we chose the text compactor online tool for summarization with different percentages and parameter.
Solar Power Generation Prediction

Predicting sun irradiance has been a crucial subject in the production of renewable energy.Prediction enhances solar system development and operation and provides several financial benefits to power companies.Statistical techniques like artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average can be used to forecast the irradiance (ARMA).However, because to their scalability or the fact that they are unable to be employed with huge data, they either lack accuracy due to their inability to capture long-term reliance.Thus, in this paper the XGBoost algorithm is implemented for prediction and Optuna Algorithm for Hyper parameter tuning and optimizing the results.Aside from predicting the solar irradiance.It is crucial to create a tool that will estimate the entire amount of energy that can be produced by a solar power plant, array, or household solar setup based on the expected solar radiation and the site's particular solar panel or array parameters.In this work methodology designed and developed a system that will not only predict the solar irradiance for next 15 days based on real time forecast but it will also predict the power generation in units for your solar power panel or array.This system is currently implemented in a webapp that can be accessed through any browser.
Information Retrieval Using Effective Bigram Topic Modeling

Many fields, such as film reviews, recommendation systems, and language processing, have effectively adopted and utilized topic modeling with Latent Dirichlet Allocation (LDA).Many texts analysis tasks, however, rely heavily on sentence construction and words to capture the meaning of text.However, word coexistence plays an important role in retrieving significant data.In this paper we present a novel method which discovers topics and topical phrases using language modeling.Proposed bigram extended LDA gives promising results to discover latent research areas in research articles and efficient classification of research articles.Experimental results are carried out to test the efficiency of proposed method.
Formative Assessment Based Students’ Recruitment Estimation: Neural Network Approach

Even though there is a strong focus on achieving the objectives of the education system, it is undoubtedly dependent on the knowledge, skills, and, most notably, the methodology of how teachers use qualitative and quantitative assessment techniques to assist learners.Teachers use formative assessment to monitor students' progress, their level of knowledge, and their ability to self-assess.One of the critical outcomes following completion of a degree program is the ability of the student to obtain employment.More specifically, it focuses on acquiring knowledge, skills, and capacities that are then applied to real-life contexts.This paper presents a neural network approach to predicting students' job placement based on formative assessment.The approach aids Higher Education Institutions in determining the progress of individual students and areas for improvement during the graduation process, increasing the likelihood of students finding employment after graduation.The paper illustrates important parameters for campus placement selection, how embedding formative assessment and neural network modelling facilitate enhancing students' knowledge, skills, and performance at the institute and fulfil its objectives of gaining meaningful employment.
Sentiment Analysis on Covid-19 Vaccination Using Machine Learning Techniques

In this research work, we have performed Machine Learning and Lexicon Based Techniques to identify and analyze user's expression or opinion on covid-19 vaccination from social media platform that is Twitter and acquainted the bulk tweets from 01 June 2021 to August 2021 using various twitter hashtags.Machine learning based Classifiers are used for investigating the evaluation performance of Algorithms.Real time datasets and machine Learning Algorithms are compared with Best Data classification Evaluation based on the size of train data also another approach is to investigating the polarity by using Lexicon Based approach for this Bing Liu Lexicons and Stanford University Lexicons are used.The global pandemic has created the medical emergency and stops the many regular activities.The whole world in the lockdown or quarantine to because Coronavirus disease.Among them, Covaxin, Covishield, Pfizer, Moderna and SputnikVare popular.Universally publicare articulating opinions on protection and success of the vaccines on social media.Research article shows, such tweets are collected from developer Application Management using a Twitter API.Unprocessed tweets are kept and preprocessed through Machine Learning techniques.Users opinion are predicted using a Classifiers Decision Tree, Support Vector Machine, K NN Algorithm and Naïve Bayes.Comparative machine learning classifiers study here comparative analysis is got highest accuracy of 97% for Decision tree with Covaxin dataset, Support vector machine with 94% for SputnikV, Naïve Bayes got highest accuracy of 95 for Covishield dataset and KNN got Highest accuracy of 96% for Covaxin.The Lexicon Based polarity classifies the score into three users opinions, positive, negative, and neutral.Result shows that, Covaxin shows 28.14% positive, 12.5% negative, and 59.36% neutral sentiment.Covishield shows 17.62% positive, 15.04% negative, and 67.34% neutral sentiment.Moderna shows 23.68% positive, 19.28% negative, and 57.02% neutral Pfizer shows 18.28% positive, 34.06% negative, and 47.66% neutral, SputnikV shows 24.62% positive, 14.1% negative, and 61.28% neutral.